DEVCOM Army Research Laboratory
Research Associateship Program (RAP)

Project Description:

Conventional static metasurfaces manipulate light waves across a 2D space, but they cannot alter the light's frequency or easily break optical reciprocity. The core research question is: How can we utilize high-speed optoelectronic materials to achieve ultrafast, pixel-by-pixel temporal modulation of a metasurface, and what unique wave phenomena, such as complete nonreciprocal light routing or dynamic frequency translation, can we demonstrate?

The primary objective of this project is to design, simulate, and characterize a sub-wavelength optoelectronic space-time metasurface capable of simultaneously shifting the phase and frequency of incoming light. By establishing precise control over both spatial and temporal gradients, the project aims to create a highly efficient, magnet-free optical isolator or a reconfigurable frequency-shifting beam-steerer applicable to next-generation telecommunications.
The participants will work closely with ARL researchers on the following tasks:

• Performing multiphysics modeling of optically controlled meta-atoms at radio frequencies.
• Running computational simulations to sweep geometrical parameters and maximize the device's modulation efficiency.
• Developing control scripts to generate specific time-varying phase patterns across the array, simulating a traveling wave profile.
• Analyzing data outputs from simulations, plotting field distributions, frequency spectrums, and efficiency curves.
• Assisting in laboratory characterization (as experimental data becomes available) by aligning optical setups, optimizing laser inputs, and capturing output beam metrics.

The participants will gain hands-on experience using engineering modeling tools, such as Ansys Lumerical or COMSOL Multiphysics, and handling high-frequency measurement systems within a photonics laboratory setting. By the conclusion of the summer program, the participants will be responsible for preparing a presentation and a technical report summarizing their research findings.

Army Research Directorate (ARD) Division: Advanced Photonics, Electronics & Quantum Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Electrical Engineering; Physics

Preferred Technical Skills:  Computer Modeling/Simulation; Electrical Engineering; Electronics; Physics; Prior experience with Python or MATLAB, along with hands-on familiarity in an optics or electronics lab, is highly advantageous for this project.

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Cho, Sang Yeon

Participant Classifications: Undergraduate Student; Graduate Student—Doctoral; Graduate Student—Master’s; Recent Undergraduate Graduate; College or University Faculty; Postdoctoral Researcher; USMA Cadet; ROTC Cadet

Project Description:

Develop methods for ranking ultra-wide bandgap (UWBG) materials and relevant dielectrics to assist in materials development that leads to improved electronic device performance. The effort will advance and integrate the metrologies of Kelvin probe, photoemission, and surface photovoltage spectroscopy to measure the energy level of optically active defects and band-to-band transitions in UWBG materials. Detection and energy-level determination of optically active defects and impurities in relevant UWBG materials and/or heterostructures will be performed.

Army Research Directorate (ARD) Division: Advanced Photonics, Electronics & Quantum Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Chemistry; Electrical Engineering; Materials Science/Engineering; Physics

Preferred Technical Skills: Electronics; Physics; Chemical Sciences/Engineering; Materials Engineering; Experience in vacuum technology and equipment interfacing.

Minumum Education Requirement: Recent Undergraduate Graduate

Mentor Name:  Birdwell, A Glen

Participant Classifications: Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral; College or University Faculty; USMA Cadet

Project Description:

Standard processing techniques developed for silicon or wide bandgap materials like GaN typically cannot be one-to-one transferred to ultra-wide bandgap materials like diamond. This project will develop atomic force microscopy (AFM) tools and techniques to characterize processing effects on diamond surfaces. The participant will assemble and qualify AFM capability on a Raman microscope system, enabling correlative spatial mapping measurement between AFM and Raman. We expect that by the project end the participate will enable our team to perform detailed mapping of the morphological and structural characteristics of diamond surfaces altered by different processing treatments. This capability will enable our team to perform root-cause analysis and process optimization based on the results of either AFM or correlative mapping.

Army Research Directorate (ARD) Division: Advanced Photonics, Electronics & Quantum Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Materials Science/Engineering; Physics; Any Major With the Required Skills

Preferred Technical Skills: Materials Engineering; Physics

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Garratt, Elias J

Undergraduate Student; USMA Cadet; ROTC Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; Recent Undergraduate Graduate

Project Description:

Over the past century, the quantum principles of superposition, electronic structure, and uncertainty relations gave us tremendous advances in a number of applications relevant to the military, including atomic clocks, magnetometry, positioning/navigation/timing (PNT), and gravimetry. While these areas can still be improved through technological advances, next-generation gains in sensing and in secure communications will occur through the concept of quantum identicality and quantum entanglement.

Our efforts conduct cross-cutting foundational research to exploit quantum effects for (1) novel sensors and capabilities, (2) beyond-classical sensor performance limits using entanglement, and (3) entanglement-enhanced information processing, decision-making, and security. Research emphasizes strong light-matter interfaces, including cavity quantum electrodynamics (QED) and nanophotonic integration. Examples include electromagnetic field sensing using Rydberg atoms, solid-state "atomic" clocks, solid-state color centers for sensing and quantum information, nanophotonics, and building blocks of entanglement distribution (quantum memories, repeaters, hybrid interfaces, etc).

Army Research Directorate (ARD) Division: Advanced Photonics, Electronics & Quantum Sciences Division

Research Location: Adelphi Laboratory Center, MD; ARL South - Austin, TX, On-site

Required Major(s): Computer Science; Electrical Engineering; Mathematics; Physics

Preferred Technical Skills: Electronics; Mathematics; Physics; Rapid Prototyping/Additive Manufacturing; Computer Modeling/Simulation; Python, embedded systems programming, quantum mechanics

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Meyer, David H

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; USMA Cadet; ROTC Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; K–12 Teacher; College or University Faculty

Project Description:

Project Objective: The objective of this project/opportunity is to implement neuromorphic circuits on FPGA board using Verilog to demonstrate Army-relevant applications, and to create a framework or modular code to create neuromorphic circuits from core building blocks (e.g. synapses, neurons, address-event representation, encoding/decoding blocks, routing fabric, timing/delay buffers, I/O interfaces).

Purpose: Provide a method to rapidly demonstrate ferroelectric-based neuromorphic circuits for Army edge computing platforms to test different options/configurations before fabrication or tape-out. Future Army platforms need to process massive amounts of sensor data in real-time under power and connectivity constraints. This project helps enable rapid prototyping of low-power, edge computing system components.

Proposed Work: Develop Verilog code modules to emulate functionality of specified neuromorphic building blocks, such as neurons, synapses, input/output encoders, and timing buffers. Then implement these building blocks on FPGA boards using AMD Vivado software suite to construct neuromorphic circuits suitable for use with spiking neural networks, connecting them with address-even representation (AER). Finally, interface the FPGA board with appropriate function generators or computers for input and with appropriate sensors/meters or computers for output.

Requisite Tool Skills: Verilog programming, AMD Vivado software suite, FPGA hardware programming, source-meters, and function generators

Desired Knowledge: Digital logic design, basic understanding of neurons and synapses, along with some exposure to neural networks

Deliverables: A functional demonstration of a specified neuromorphic circuit (e.g. ferroelectric crossbar array with peripheral and readout circuits) that can accept a specified input and provide an expected output.

Army Research Directorate (ARD) Division: Advanced Photonics, Electronics & Quantum Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Electrical Engineering; Computer Engineering; Software Engineering; Robotics/Autonomous Systems; Neuroscience; Mathematics; Physics; Any Major With the Required Skills

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Computer Science; Electrical Engineering; Electronics; AMD Vivado, Verilog, Python

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Chin, Matthew L

Participant Classifications: Undergraduate Student; USMA Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; Recent Undergraduate Graduate

Project Description:

Silicon carbide (SiC) is an established wide-bandgap semiconductor widely used in power electronics, yet its point defects remain underexplored as practical solid-state spin qubits for quantum technologies. The core research problem is how to harness these defects—within different SiC polytypes and device architectures—to enable coherent quantum networks that also incorporate sensing functionality. The project’s objective is to advance ARL’s efforts by demonstrating SiC-based qubits as viable candidates for integrated quantum systems. Participants will perform work in one or more of three complementary areas: epitaxial growth of low-strain, low-defect-density SiC films by chemical vapor deposition (CVD) followed by rigorous materials characterization; modeling and nanofabrication of photonic-crystal structures to enhance light–matter interactions; or design, processing, and wafer-scale demonstration of hybrid devices that co-integrate quantum, optoelectronic, MEMS, and power-electronic components. Methods and tools may include CVD, standard semiconductor process flows (lithography, etching, deposition), magnetic-resonance techniques, optical spectroscopies, electrical measurements, and structural probes. The expected outcome is a set of validated low-defect SiC materials, photonic nanostructures, or functional hybrid prototypes that establish SiC as a manufacturable platform for solid-state quantum networks and multi-modal sensing.

Army Research Directorate (ARD) Division: Advanced Photonics, Electronics & Quantum Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Chemical Engineering; Chemistry; Electrical Engineering; Materials Science/Engineering; Physics

Preferred Technical Skills: Chemical Sciences/Engineering; Electrical Engineering; Materials Engineering; Physics

Minumum Education Requirement: Undergraduate Student

Mentor Name:  VanMil, Brenda

Participant Classifications: Graduate Student—Doctoral; Graduate Student—Master’s; Recent Undergraduate Graduate; Undergraduate Student

Project Description:

There are challenges in predicting the most appropriate units for civilian and military deployments across the National Guard. Army Vantage presents a unified common operating picture to provide more accurate force readiness predictions. This capstone can help build on prior Soldier led software development to provide readiness predictions for the California Military Department (CMD).

Army Research Directorate (ARD) Division: Advanced Photonics, Electronics & Quantum Sciences Division

Research Location: ARL West - Los Angeles, CA, Remote

Required Major(s): Data Science; Computer Science; Information Systems

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Augmented Reality/Virtual Reality; Communication and Networking Technologies; Computer Modeling/Simulation; Computer Science; Cybersecurity; Data Analysis; Data Science; Data Visualization; Geographic Information Systems; Human Factors Engineering/Human Systems Integration; Program or Project Management; Policy Research; Web/Application Development; UI/UX Design; already have a CAC; prior experience using Palantir, i.e., Army Vantage

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Khooshabehadeh, Peter

Project Description:

The goal of this project is to investigate the concept of objective-driven shared autonomy for uncrewed aerial system (UAS) swarms through the development of a minimal simulation based proof of concept. UAS have revolutionized the ability to operate in critical situations such as disaster response. However, operators remain responsible for supervising and micromanaging individual vehicles or small centralized groups, limiting scalability and creating single points of failure. This project investigates how operators can communicate mission objectives rather than individual vehicle commands, enabling autonomous agents to collaboratively determine task allocation, coordination, and adaptation to dynamic environments.

Participants will design and implement a proof-of-concept simulation framework for decentralized multi-agent autonomy, enabling a single operator to communicate mission objectives to a team of UAS. The project focuses on decentralized communication, multi-agent coordination, and autonomous decision-making through representative scenarios such as collaborative mapping. In detail, the tools/tech participants will use include software development environments (Python/C++), gain experience in computer science or robotics, based on their interest in autonomous and multi-agent systems.

Expected outcome:
Multi-agent communication and coordination, autonomous decision-making architectures, multi-agent simulation and software architecture, objective-driven shared autonomy, systems engineering

Army Research Directorate (ARD) Division: Advanced Photonics, Electronics & Quantum Sciences Division

Research Location: ARL West - Los Angeles, CA, Hybrid

Required Major(s): Aerospace/Aeronautical Engineering; Computer Engineering; Computer Science; Electrical Engineering; Human Factors/Human Systems; Industrial/Systems Engineering; Mechanical Engineering; Robotics/Autonomous Systems; Software Engineering; Mathematics; Statistics; Any Major With the Required Skills

Preferred Technical Skills: Advanced Sensing; Aerospace Engineering; Artificial Intelligence/Machine Learning; Computer Modeling/Simulation; Computer Science; Communication and Networking Technologies; Electrical Engineering; Electronics; Mathematics; Mechanical Engineering; Software Development; UI/UX Design; Human Factors Engineering/Human Systems Integration; Python/C++ ; Autopilots: PX4 or Ardupilot, familiarity with UAS/drones, embedded systems software engineering

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Khooshabehadeh, Peter

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; USMA Cadet; ROTC Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; K–12 Teacher; College or University Faculty

Problem: sUAS operating as autonomous, unattended devices have limited power resources; typically limited to the sUAS integral battery. An additional complicating factor is that any solution must be remotely deliverable to the area the sUAS will operate.

Project Objective: Design an air-deliverable (dropped from a large quadcopter) sUAS recharging station that self-deploy on the ground, provide two full recharge cycles to a sUAS, and recharge its own power bank using solar energy technology.

The work the participant will perform: Create system and subsystem specification, identify key material, create mechanical and electric engineering diagrams, create prototypes of critical system components, air delivery and power regeneration.

Methods, tools, or technologies that may be used: Solid Works and KiCAD Electrical and mechanical design applications.
Expected Outcome and Deliverable: Prototype electrical and mechanical solutions for key components of the solution.

Army Research Directorate (ARD) Division: Advanced Photonics, Electronics & Quantum Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Mechanical Engineering; Electrical Engineering; Aerospace/Aeronautical Engineering; Robotics/Autonomous Systems

Preferred Technical Skills: Electrical Engineering; Mechanical Engineering

Minumum Education Requirement: Recent Undergraduate Graduate

Mentor Name: Houser, Jeffrey G

Participant Classifications: Recent Undergraduate Graduate; Graduate Student—Master’s

Project Description:

This summer internship project will develop skills related to understanding and utilizing fundamental structure processing property relationships in steel. Specifically the project will focus on how microstructural changes during quenching of martensitic steels affect their subsequent tempering response. Various equipment from tensile testing machines to thermomechanical simulators will be used to design a set of experiments to test the effects of novel heat treatments on mechanical response. Technical training on new tools and techniques will be provided throughout the summer internship in addition to professional development opportunities and experiences within DEVCOM ARL and across APG.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Materials Science/Engineering; Mechanical Engineering

Preferred Technical Skills: Materials Engineering

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Rupinen, Michael C Jr

Participant Classifications: Undergraduate Student

Project Description:

Material degradation driven by environmental corrosion presents a persistent operational and lifecycle challenge for critical structural systems and equipment. This research project focuses on elucidating early-stage corrosion mechanisms, evaluating protective surface treatments, and quantifying degradation kinetics across varied substrates under varied exposure conditions. The participant will conduct both laboratory-based characterization and computational data comparison. Hands-on responsibilities include preparing coupons, executing exposure testing, and conducting electrochemical evaluations such as potentiodynamic polarization. To assess localized structural and chemical changes, the participant will utilize surface analysis techniques, including Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), and 3D optical surface profilometry. Concurrently, the participant will report findings through datasets to forecast trends.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): n/a

Preferred Technical Skills: Chemical Sciences/Engineering; Materials Engineering; Data Analysis

Minumum Education Requirement: n/a

Mentor Name:  Blohm, Lindsey M

Project Description:

This project seeks to understand the influence of gradient composition in the deformation of infrared (IR) windows when subjected to extreme environments. To optimize sensor performance (e.g., accuracy when tracking a target), targeting error must be minimized. However, these windows can undergo bowing due to thermal gradients as one side of the the material expands more than the other. This results in refraction of incoming light, thus reducing the accuracy of target tracking. Introducing gradient compositions would allow for more control over the temperature gradient and the thermal expansion, thus improving performance.

The participant will utilized developed computational capabilities, primarily finite element models (FEniCS), to evaluate the effect of both through thickness and radial gradients on the evolution of the temperature, stress, and deformation of potential materials. High performance computing resources will be utilized for creating FEniCS meshes for gradient designs and performing coupled thermomechanical simulations. The intern will develop workflows to a systematic study of how the magnitude and rate of gradation affect the resultant curvature of the window.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site; Remote; Hybrid

Required Major(s): Aerospace/Aeronautical Engineering; Materials Science/Engineering; Mechanical Engineering; Physics; Any Major With the Required Skills

Preferred Technical Skills: Computer Modeling/Simulation; Materials Engineering; Mechanical Engineering; Python, Finite Element Modeling

Minumum Education Requirement: Recent Undergraduate, Graduate

Mentor Name:  Guziewski, Matthew C

Participant Classifications: Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher

Project Description:

This position is open to US citizens only. We are seeking a motivated student to join our team for research in strain engineering, which focuses on modifying microorganisms to enhance their capabilities in producing valuable chemicals, improving stress resilience, or optimizing metabolic pathways. In this role, you will have the opportunity to work with cutting-edge genetic engineering techniques, including modifying gene expression, and evaluating performance under different culture conditions.
We welcome applicants with experience in the following areas:
• Biology
• Genetics
• Synthetic Biology
• Microbiology
• Systems biology
• Bioengineering
Some preferred skills include molecular cloning techniques, RT-qPCR, microbiology techniques, aerobic microorganism culture work, media formulations, and plate reader assays. This is an opportunity to grow skillsets in synthetic biology and biomanufacturing in a high caliber research environment.

The major deliverable is identifying methods to increase flux towards a desired pathway through genetic engineering and/or bioreactor method development.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: ARL South - Austin, TX, On-site

Required Major(s): Biological Sciences; Chemical Engineering; Chemistry

Preferred Technical Skills: Biological Sciences/Engineering; Chemical Sciences/Engineering. Preferred Technical Skills:  Some preferred skills include molecular cloning techniques, RT-qPCR, microbiology techniques, aerobic microorganism culture work, media formulations, and plate reader assays.

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Pullen, Randi M

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher

Project Description:

This position is open to US citizens only. We are seeking a driven student to join our team in a computational biology project focused on writing scripts to automate data workflows and streamline experimental planning and data curation. You will help develop and implement scripts that bridge input and output formats for robotic systems and curate data for complex analysis pipelines. This role will also involve designing automation tools for experimental planning and integrating FAIR (Findable, Accessible, Interoperable, Reusable) data principles into the design-build-test-learn cycle. You will collaborate with scientists to ensure data flows seamlessly across different platforms, contributing to more efficient and reproducible research.
We welcome applicants with an interest or background in:
• Computational Biology
• Computer Science
• Software engineering
• Bioinformatics
• Scripting Languages (e.g., Python, R)
• Data Analysis and Integration
• Robotics and Automation
The expected outcomes are user interfaces that enable wetlab biologists to design experiments, program various robotic platforms, and facilitate genetic construct design in high throughput.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: ARL South - Austin, TX, On-site

Required Major(s): Biological Sciences; Chemical Engineering; Computer Science; Data Science/Analytics; Software Engineering; Statistics; Robotics/Autonomous Systems

Preferred Technical Skills: Computer Science; Data Analysis; Data Visualization; Data Science; Software Development; Statistics; UI/UX Design; Rapid Prototyping/Additive Manufacturing. Preferred Technical Skills:  R, python, user interface design, statistical modeling

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Pullen, Randi M

Participant Classifications: Undergraduate Student; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; Recent Undergraduate Graduate

Project Description:

This position is open to US citizens only. We are seeking a biochemistry student to join our team in a research project focused on chemical identification and strain characterization using advanced analytical techniques. In this role, you will work with mass spectrometry (MS), high-performance liquid chromatography (HPLC), and colorimetric plate reader assays to analyze chemical compounds produced by engineered microbial strains. This project will give you hands-on experience in sample preparation, operation of state of the art systems, and data analysis to characterize metabolites, proteins, or other biomolecules. You will collaborate with researchers to uncover the chemical profiles of different strains, contributing to breakthroughs in biotechnology and synthetic biology.
We welcome applicants with experience in:
• Analytical Chemistry
• Mass Spectrometry (MS)
• High-Performance Liquid Chromatography (HPLC)
• Metabolomics
• Bioreactors/aerobic fermentations

Some preferred skills include: HPLC, FPLC, protein purification, plate reader assays, aerobic microorganism culture work, media formulations, and microbioreactors. This is an opportunity to grow skillsets in synthetic biology and biomanufacturing in a high caliber research environment.
The expected outcome is a method to "fingerprint" engineered strains in as high throughput as possible to contribute towards predictive biodesign for improved production yields.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: ARL South - Austin, TX, On-site

Required Major(s): Biological Sciences; Chemical Engineering; Chemistry; Data Science/Analytics

Preferred Technical Skills: Biological Sciences/Engineering; Chemical Sciences/Engineering. Preferred Technical Skills:  Some preferred skills include: HPLC, FPLC, protein purification, plate reader assays, aerobic microorganism culture work, media formulations, and microbioreactors.

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Pullen, Randi M

Participant Classifications: Undergraduate Student; Graduate Student—Doctoral; Graduate Student—Master’s; Postdoctoral Researcher

Project Description:

The Army Research Laboratory (ARL) is in the frontier of developing novel materials and manufacturing processes that enable production of Army capabilities with novel performance at unprecedented rates and lower costs. Candidates will work hands on with advanced manufacturing techniques and equipment in a laboratory environment to produce material coupons and hardware for testing and analysis. Research projects may include development and integration of robotics, cold spray, materials characterization (metallography, specimen preparation, mechanical testing, microscopy, x-ray diffraction), and a variety of 3D printing (additive manufacturing) techniques of metallic (laser powder bed fusion, electron beam powder bed fusion, directed energy deposition), polymeric (stereolithography, fused filament deposition, inkjet, selective laser sintering) , and electronic materials. Projects may be tailored to the participants experience to include: numerical modeling of processes and materials; microstructure-processing-property-relations; powder processing (milling, sizing, compaction and sintering); and machine learning and data analytics. Participants will work with ARL researchers to contribute to R&D project outcomes and interact directly with soldiers to understand Army needs for advanced manufacturing solutions to battlefield challenges.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Preferred Technical Skills: Aerospace Engineering; Artificial Intelligence/Machine Learning; Augmented Reality/Virtual Reality; Computer Modeling/Simulation; Data Science; Data Visualization; Electrical Engineering; Electronics; Materials Engineering; Mechanical Engineering; Physics; Rapid Prototyping/Additive Manufacturing

Minumum Education Requirement: Undergraduate Student

Mentor Name: Mcwilliams, Brandon A

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; USMA Cadet; ROTC Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; K–12 Teacher; College or University Faculty

Project Description:

Support structures in metal additive manufacturing allow for easy part removal from build plates, thermal dissipation and support for overhanging features. Although used extensively in laser powder bed fusion, currently, support structures are not well developed for systems with coarser deposition such as laser powder directed energy deposition (LP-DED). This project focuses on the feasibility of developing support structures for LP-DED either through processing parameter control or through design solutions. The objective is to design and test several support structure designs to be manufactured by LP-DED. Work will include development, printing and mechanical testing of potential support structure designs. Some of the tools available include SolidWorks software for 3D CAD design and printing on one of our LP-DED systems. Initial mechanical testing will likely occur through mechanical (pliers) removal, but more quantitative testing may occur if time allows. Deliverables will include documentation of the designs and the respective performance through a presentation and a report.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Materials Science/Engineering; Mechanical Engineering

Preferred Technical Skills:  Materials Engineering; Mechanical Engineering; Rapid Prototyping/Additive Manufacturing, SolidWorks

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Mock, Clara M

Participant Classifications: Graduate Student—Master’s; Graduate Student—Doctoral; USMA Cadet; ROTC Cadet

Project Description:

We are seeking motivated, hands-on engineering interns to join our research team this summer. This project focuses on overcoming the processing complexities of fiber-reinforced, high-temperature composite materials to enable the next generation of defense and aerospace technologies. Fiber-reinforced composites are highly advantageous because of their high strength-to-weight ratios. Processing and manufacturability of composite materials, however, can be very complex. In this project, students will investigate material processing and characterization of high-temperature composite materials. Students will fabricate composites and evaluate physical, mechanical, and thermal properties. Ideal candidates are majoring in materials science, chemical, or mechanical engineering and would enjoy hands-on experimentation.
Experimentation may include any of the following:
• Chemical synthesis or electrophoresis deposition
• Study of processing variables such as time, temperature, or pressure and impacts of those changes on the material microstructure and properties
• Microstructural characterization such as scanning electron microscopy or X-ray computed tomography
• Thermal analysis evaluation
• Mechanical testing of composite materials

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Chemical Engineering; Materials Science/Engineering

Preferred Technical Skills: Chemical Sciences/Engineering; Materials Engineering; Mechanical Engineering

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Sietins, Jennifer M

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; USMA Cadet; ROTC Cadet; Graduate Student—Master’s; Graduate Student—Doctoral

Project Description:

Opportunities exist for fundamental research and development (R&D) efforts towards enabling the next generation ceramics and ceramic composites for Army systems. Research activities include: 1) novel synthesis and processing techniques for opaque and transparent ceramics and composites with optimal structure/properties for extreme environments and high-rate impact, 2) advanced manufacturing science for development of heterogeneous multi-scale ceramics and interfaces with high resistance to thermomechanical stress, fracture/failure, and ablation/oxidation, 3) high-throughput simulation, machine learning and design optimization for processing-structure-property relationships, and 4) high-throughput non-destructive evaluation and characterization for materials discovery.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Chemical Engineering; Aerospace/Aeronautical Engineering; Computer Science; Electrical Engineering; Materials Science/Engineering; Mechanical Engineering

Preferred Technical Skills:  Artificial Intelligence/Machine Learning; Chemical Sciences/Engineering; Computer Modeling/Simulation; Data Science; Electrical Engineering; Materials Engineering; Mechanical Engineering

Preferred attributes include a strong knowledge of ceramic engineering principles and analytical and mechanical characterization techniques. Specialized expertise also desired in areas of ceramic synthesis methods, inorganic chemistry, colloidal particle suspension dispersion and rheology, advanced microscopy and spectroscopy techniques, high-rate mechanisms, advanced manufacturing, process control and modeling, and AI/ML techniques.

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Vargas-Gonzalez, Lionel R

Participant Classifications: USMA Cadet; ROTC Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; College or University Faculty; Postdoctoral Researcher; Undergraduate Student; Recent Undergraduate Graduate

Project Description:

The United States is dangerously dependent on foreign sources of critical materials relevant to national security such as manganese, tungsten, germanium, nickel, copper, and rare earth elements. Opening new mines is challenging because of extensive permitting requirements, environmental health and safety concerns, and the large capital expenditures required. Alternative sources of these materials, such as rock dumps, tailings, and slag, are worth exploring because of the relative speed of permitting, environmental review, and historical records that provide compositional insight into the deposits including the relative abundance of critical materials in these waste streams.

Bioleaching, or the use of organisms to separate desired elements from ore and waste, is of increasing interest because it is a low energy and passive approach, potentially well-suited to valorize non-ore materials. Leaching can be assessed using inductively-coupled plasma mass spectrometry (ICP-MS) methods, however ICP-MS can be slow and labor-intensive. Improving bioleaching efficiency requires a higher-throughput assay that could screen many mutant variants at a time.

The objective and the expected outcome of the project is to develop a high-throughput assay for the detection of a range of elements, potentially including copper, cobalt, zinc, and germanium. We will use this assay in the assessment of mutant strains of Acidithiobacillus ferrooxidans for their ability to promote leaching from these mine samples. During the project, the participant will use and run colorimetric assays in 96-well plate format, generate mutant libraries of A. ferrooxidans, and confirm findings using ICP-MS. Other methods, tools, and technologies used will include bacterial conjugation/cloning and cell culture.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Biological Sciences; Chemistry; Materials Science/Engineering

Preferred Technical Skills: Biological Sciences/Engineering; Chemical Sciences/Engineering; Materials Engineering

Minumum Education Requirement: Undergraduate Student

Mentor Name: Kozlowski, Mark T

Participant Classifications: Graduate Student—Doctoral; Graduate Student—Master’s; Undergraduate Student

Project Description:

Accelerating materials discovery is key to delivering novel materials solutions. One rate limiting step in the materials development chain is the evaluation of mechanical properties, particularly ductility and fracture properties. To address the challenge, high-throughput sample fabrication and testing methodologies must be developed. One solution is miniaturized mechanical testing enabled by femtosecond laser micromachining of millimeter scale specimens. In this opportunity, the participant will apply small scale mechanical testing to develop high throughput mechanical testing techniques for parameterizing material models. Loading methods include tension, compression, and novel geometries for fracture. Available testing environments include low temperatures up to extreme temperatures through laser heating. These approaches will be applied to Army relevant materials such as advanced high strength steels, additively manufactured metals, or functionally graded materials. The outcomes of this research will contribute to current and future DEVCOM ARL materials development programs.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Materials Science/Engineering; Mechanical Engineering

Preferred Technical Skills: Data Science; Materials Engineering

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Magagnosc, Daniel J

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; Graduate Student—Master’s

Project Description:

This project supports DEVCOM Army Research Laboratory priorities in foundational research on extreme materials. Specifically, it addresses the need to understand and develop material systems capable of maintaining structural and thermal performance in hypersonic applications. This capstone project will investigate direct energy deposition pathways for functionally graded materials (FGMs) that transition from conventional hot-structure materials to refractory thermal-barrier material systems for hypersonic vehicle applications. The research will investigate their high-temperature capability, density, thermal conductivity, oxidation resistance, manufacturability, and probable failure mechanisms under hypersonic thermal environments. Particular attention will be given to graded material interfaces, coefficient-of-thermal-expansion mismatch, residual stress development, cracking, and oxidation protection. The project will also assess how fabrication methods influence material microstructure, bonding quality, and performance across compositional gradients. The final product will identify promising FGM architectures and manufacturing approaches that could improve the thermal durability, structural integrity, and service life of future hypersonic vehicle hot structures. Student skills developed may include CALPHAD-based thermodynamic/kinetic modeling, thermal modeling, and introductory aerodynamic-heating analysis.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): n/a

Preferred Technical Skills:Artificial Intelligence/Machine Learning; Materials Engineering; Computer Modeling/Simulation; Mechanical Engineering; Rapid Prototyping/Additive Manufacturing; Experience with CALPHAD is a plus.

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Haines, Christopher D

Project Description:

Modern Army systems rely on advanced materials that must withstand extreme operational environments, including intense thermal exposure, severe mechanical loads, and physical impacts. Characterizing how novel high-temperature materials, advanced protective systems, and additive manufacturing (AM) components deform and fail under these diverse conditions is a critical engineering challenge. The objective of this summer research project is to evaluate the mechanical response of these next-generation materials across various loading rates and environmental conditions, while optimizing the diagnostics used to measure them. In this effort, you will work alongside multidisciplinary researchers to conduct mechanical characterization experiments—spanning quasi-static to dynamic loading rates—using non-contact Digital Image Correlation (DIC) to capture full-field, transient strain and displacement data in real-time. To accomplish this, you will utilize state-of-the-art instrumentation, including mechanical load frames, dynamic impact tools, and advanced DIC analysis software. The expected deliverables include experimental datasets and analytical models that describe the material response across a range of loading rates and environmental conditions.

Army Research Directorate (ARD) Division: Biological, Materials & Manufacturing Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Aerospace/Aeronautical Engineering; Materials Science/Engineering; Mechanical Engineering

Required Major(s): Materials Engineering; Mechanical Engineering

Preferred Technical Skills: Mechanical testing

Minumum Education Requirement: Undergraduate Student

Mentor Name: O'Brien, Daniel J

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral

Project Description:

Accurate recognition and tracking of people observed from a distance and in diverse poses—particularly individuals in distress during chaotic events—are essential for applications such as disaster response, damage assessment, and rapid, distress-aware battlefield triage. These capabilities have been increasingly enabled by advances in artificial intelligence integrated with unmanned platforms, including unmanned aerial vehicles (UAVs). Identifying injured or distressed individuals through pose analysis is a critical capability in complex, real-world environments. However, pose identification using video captured by unmanned systems remains challenging because of platform vibration, limited camera resolution, uncertain camera poses, and other adverse operating conditions. The scarcity of representative human-pose data creates an additional barrier to robust and reliable performance. To address these challenges, this research aims to advance the underlying science of human and casualty identification and injury-severity assessment for rapid triage. In particular, the participant will contribute to the development of real-time injury-severity assessment capabilities using vision-language models (VLMs).

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Computer Science; Computer Engineering; Data Science/Analytics; Electrical Engineering

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Computer Science; Data Analysis; Electrical Engineering; Strong background in computer vision, deep learning, and machine learning
Experience with vision-language models (VLMs) and multimodal large language models Proficiency in Python and deep-learning frameworks such as PyTorch

Minumum Education Requirement: Graduate Student - Master's

Mentor Name: Kwon, Heesung

Participant Classifications: Graduate Student—Doctoral; Graduate Student—Master’s

Project Description:

As artificial intelligence (AI) systems continue to grow in capability and complexity, there is increasing interest in methods for selectively removing the influence of specific data, knowledge, or behaviors from trained models. This research project will explore machine unlearning as a potential approach for safeguarding AI systems and improving their adaptability in changing operational or data environments.

The objective of this project is to investigate research questions related to how AI models can be updated, refined, or modified to reduce the impact of targeted information without requiring complete retraining in every case. The participant may support a range of research activities, such as reviewing relevant literature, assisting with data preparation and annotation, helping develop experimental approaches, implementing or adapting prototype methods, conducting tests, and analyzing results.

Depending on project needs, the work may involve machine learning frameworks, data analysis tools, model evaluation techniques, annotation workflows, and other research software commonly used in AI development. Specific methods and tasks may vary based on project direction, technical maturity, and research priorities at the time of execution.

Expected outcomes may include a summary of relevant findings, experimental results, annotated datasets or supporting research artifacts, prototype implementations, or recommendations for future research. The project is intended to provide flexibility for evolving research goals while giving participants exposure to current challenges in machine unlearning and AI safeguarding.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Computer Science; Robotics/Autonomous Systems

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Computer Science; Mathematics; Natural Language Processing

Minumum Education Requirement: Graduate Student - Master's

Mentor Name: Eum, Sungmin

Participant Classifications: Graduate Student—Master’s; Graduate Student—Doctoral; ROTC Cadet; USMA Cadet

Project Description:

A core challenge in modern electromagnetic warfare has adversaries use multiple mobile radio-frequency (RF) emitters for communication, jamming, and interference while continuously changing position and operating within dynamic, hard-to-predict spectrum conditions. The research problem posed here is how to accurately track the locations of these emitters and characterize their effects on the RF environment when only sparse measurements of the battlespace are available.

The project’s objective is to develop a low-latency, self-supervised learning architecture capable of tracking multiple unknown, moving RF emitters using sparse received signal strength (RSS) data and multimodal inputs such as RGB and infrared (IR) imagery. The participant will design, test, and refine this architecture to minimize mean-square error between estimated and actual emitter locations, while also supporting decentralized implementation and edge deployment.

The work will rely on Python and PyTorch for model development, NVIDIA Sionna for ray-tracing-based generation of RF propagation and RSS maps across varying emitter configurations, carrier frequencies, and terrain settings, and joint embedding predictive architectures for multimodal self-supervised learning. Using simulated data, the participant will evaluate system performance and improve robustness under realistic operational conditions.

The expected deliverable is an algorithmic framework for multimodal RF emitter tracking, accompanied by supporting code, software artifacts, and technical documentation.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Computer Engineering; Data Science/Analytics; Electrical Engineering; Statistics; Computer Science

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Communication and Networking Technologies; Computer Modeling/Simulation; Data Analysis; Data Visualization; Electrical Engineering; Statistics; Python

Minumum Education Requirement: Graduate Student - Master's

Mentor Name:  Schizas, Ioannis D

Project Description:

As artificial intelligence systems increasingly support human decision making, users must often infer information that is not directly available, such as system intent, relevant context, relationships among information, uncertainty, or the basis for a recommendation. This project investigates inferential burden, defined as the cognitive effort required to reconstruct information needed for reasoning when that information is incomplete, ambiguous, fragmented, or indirectly represented.

The objective is to determine whether inferential burden can be systematically measured and whether changes in information representation predict differences in reasoning performance. The research will contribute to development and initial evaluation of a potential Inferential Burden Index for studying human–AI interaction.

The student researcher will assist in developing experimental materials, preparing and administering research sessions, collecting and organizing behavioral and survey data, conducting data-quality checks, and performing preliminary statistical analyses. Experimental conditions may vary factors such as availability of contextual information, visibility of relationships among information, uncertainty, or persistence of relevant information. Measures may include decision accuracy, response time, confidence, comprehension, perceived workload, and subjective assessments of inferential difficulty.

Methods may include controlled human-participant experiments, computer-based decision tasks, survey instruments, and statistical analysis using tools such as Python, R, or comparable analysis software, subject to required research approvals.

Expected outcomes include a curated research dataset, analysis of factors contributing to inferential burden, and results supporting a research report, conference presentation, or scholarly publication.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: ARL South - Austin, TX, On-site

Required Major(s): Human Factors/Human Systems

Preferred Technical Skills: Human Factors Engineering/Human Systems Integration; Statistics; Data Visualization; Data Analysis. Experience with statistical analysis or behavioral research methods is preferred. Familiarity with Python, R, MATLAB, SPSS or similar data-analysis software would be useful but is not required. Experience with human-subjects research, experimental design, survey tools, data visualization or quantitative analysis would also be beneficial.

Minumum Education Requirement: Undergraduate Student

Mentor Name:   Hansberger, Jeffrey T

Participant Classifications:Undergraduate Student; Recent Undergraduate Graduate; ROTC Cadet; Graduate Student—Master’s; Graduate Student—Doctoral

Project Description:

Cyber analysts frequently make decisions using incomplete, distributed, and uncertain information drawn from alerts, logs, threat intelligence, incident reports, and other data sources. As artificial intelligence is increasingly incorporated into cybersecurity workflows, there is a need to better understand how analysts form hypotheses, integrate evidence, assess uncertainty, and decide when to trust or question AI-supported recommendations.

The objective of this project is to characterize key cognitive and information-processing demands in cyber analytic reasoning and identify opportunities for AI-assisted decision support. The student researcher will help review relevant literature and public cyber incident material, develop representative analytic scenarios, identify common decision points and information requirements, and map how evidence is used to form and revise hypotheses. The student may also help develop structured representations of analyst reasoning, such as evidence chains, timelines, relationship graphs, or decision models.

Methods may include literature review, cognitive task analysis techniques, structured scenario analysis, analysis of publicly available cybersecurity datasets or case studies, and qualitative or quantitative coding. Tools may include Python, R, spreadsheet-based analysis, visualization software, or other data-analysis environments as appropriate.

Expected outcomes include a documented model of cyber analytic reasoning, a set of representative decision scenarios and information requirements, identification of cognitive bottlenecks and potential AI-support opportunities, and results suitable for a technical report, student presentation, or future research publication.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: ARL South - Austin, T, On-site; Remote; Hybrid

Required Major(s): Cybersecurity; Psychology

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Cybersecurity; Human Factors Engineering/Human Systems Integration; Data Visualization; Data Science. Familiarity with cybersecurity concepts, network security, cyber incident analysis, or threat intelligence is preferred. Exposure to log analysis, security information and event management tools, public cyber datasets, or data visualization would also be beneficial.

Minumum Education Requirement: Undergraduate Student

Mentor Name: Hansberger, Jeffrey T

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; ROTC Cadet; Graduate Student—Master’s; Graduate Student—Doctoral

Project Description:

This project investigates NPL methods and LLMs toward developing various military applications. The objective of this project is to develop language technologies to support Warfighters operating in the Americas, Europe, Middle East, and Indo-Pacific region. The participant will assist ARL researchers in developing training data and fine-tuning language models to enable a new generation of speech translation systems, optimizing performance and maintaining high-quality standards on small hardware platforms. The participant will work with multi-disciplinary teams, including researchers, software engineers, and computational linguists and complete the tasks as assigned including developing knowledge product, training data, and software components.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Computer Science; Computer Engineering; Data Science/Analytics; Foreign Language Studies

Preferred Technical Skills: Data Science; Computer Science; Natural Language Processing. NLP and pre-trained LLM, Linux, Python

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Larocca, Stephen A

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; USMA Cadet; ROTC Cadet; Graduate Student—Master’s; Graduate Student—Doctoral

Project Description:

How can we measure whether people and intelligent systems are adapting effectively to one another? This project will investigate Complexity Synchronization, a data-driven framework for quantifying coordinated changes across human teammates and artificial intelligence (AI) agents. The goal is to determine whether patterns of synchronization can provide useful indicators of team coordination, trust, workload, stress, or impending performance problems.

The student will contribute to a pilot study using either a human–agent or multi-human teaming paradigm. Activities may include helping design experimental tasks; preparing, synchronizing, and quality-checking multimodal time-series data; analyzing human behavioral and physiological signals; and examining AI behavior, decision variables, or adaptation parameters. Depending on the study design, data may include EEG or fNIRS, cardiovascular and respiratory measures, eye tracking, movement or posture, surface electromyography, keystrokes, button presses, and task-performance measures.

Students may use Python or MATLAB; signal-processing, statistical, and machine-learning methods; time-series analysis; visualization tools; and techniques such as detrended fluctuation analysis, diffusion entropy analysis, cross-correlation, and dynamic network analysis. Students from computer science, electrical or biomedical engineering, physiology, neuroscience, psychology, human–computer interaction, data science, applied mathematics, or related fields are encouraged to apply.

The expected outcome is a documented analysis pipeline and preliminary results showing how complexity-based measures relate to human and team behavior. Deliverables may include software, visualizations, a technical report, and a presentation of findings. Prior experience with physiological data, human-subjects research, or AI systems is helpful but not required; training will be provided.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location:  Aberdeen Proving Ground, MD, Hybrid

Required Major(s): Biological Sciences; Biomedical Engineering; Computer Engineering; Computer Science; Electrical Engineering; Human Factors/Human Systems; Mathematics; Neuroscience; Psychology; Robotics/Autonomous Systems; Any Major With the Required Skills

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Biological Sciences/Engineering; Computer Science; Data Analysis; Human Factors Engineering/Human Systems Integration; Statistics

Minumum Education Requirement: Undergraduate Student

Mentor Name: Bradford, Jessica Cortney (Cortney)

Participant Classifications: High School Student; Undergraduate Student; Recent Undergraduate Graduate; USMA Cadet; ROTC Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; College or University Faculty

Project Description:

This project seeks to discover how humans can interact with multiple modalities of information to uncover complementary or conflicting narratives. This project will conduct research into the design of immersing a human into a 3D reconstruction to visually investigate their surroundings, and for the human to engage in dialogue with a robot or virtual agent about the space. The human can cross-reference written sources with visual cues to reveal deception, man-made or as a result of overlooking. The internship will involve the scoping of a research question that fits within these areas of deception, trust, and user interaction paradigms for dialogue and exploration. The intern will design an actionable research plan, conduct the plan and adapt to unforeseen circumstances, and document findings in an academic paper draft. Possible focused topics include developing labeled corpora, experimenting with signature models or developing novel workflows, implementing proof-of-concept dashboards and interaction paradigms, and/or designing evaluation criteria for assessing state-of-the-art and new models.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: Adelphi Laboratory Center, MD; ARL West - Los Angeles, CA, Hybrid

Required Major(s): Computer Science; Human Factors/Human Systems; Psychology; Robotics/Autonomous Systems; Software Engineering; Any Major With the Required Skills; Computational Linguistics

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Natural Language Processing. Interest and/or experience in computer science, AI, human-agent communication

Minumum Education Requirement: Undergraduate Student

Mentor Name: Bonial, Claire N

Participant Classifications: USMA Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; College or University Faculty; Recent Undergraduate Graduate; Undergraduate Student

Project Description:

This project seeks to discover how humans trust (or distrust) what they read in today’s complex information environment. This project will conduct research into how to present evidence of deception found in natural language texts. A critical element of this work will be how to provide explanations of evidence to gain, not sacrifice, the human’s trust. The internship will involve the scoping of a research question that fits within these areas of deception, trust, and user interaction paradigms for labeling or exploration. The intern will design an actionable research plan, conduct the plan and adapt to unforeseen circumstances, and document findings in an academic paper draft. Possible focused topics include developing labeled corpora, experimenting with signature models or developing novel workflows, implementing proof-of-concept dashboards and interaction paradigms, and/or designing evaluation criteria for assessing state-of-the-art and new models.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: Adelphi Laboratory Center, MD; ARL West - Los Angeles, CA, Hybrid

Required Major(s): Computer Science; Human Factors/Human Systems; Robotics/Autonomous Systems; Any Major With the Required Skills; Computational Linguistics

Preferred Technical Skills: Computer Science; Artificial Intelligence/Machine Learning; Natural Language Processing. Interest and/or experience in computer science, human-agent interaction, and natural language processing.

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Bonial, Claire N

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; USMA Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; College or University Faculty

Project Description:

Predictive Analytics: Massive data sets describe the movement of people and things around the country and the world. A significant amount of effort has gone into the creation of look-back analytics that help analysts understand what has happened in the past with these datasets. However, far less has been done to look forward in time to predict the future and/or risks. With this project, researchers will investigate the creation of ML models and other algorithms for use in the prediction of things like:
- The location and type of disruptive incidents (like crashes or disturbances on transportation facilities)
- The duration of a crash (e.g. the amount of time it will take for the crash to be resolved and for traffic to return to normal)
- The movements of aircraft and marine vessels. E.g. the route and/or destination of a plane or ship. People movement. Given certain environmental conditions, where might people travel to.
- Travel times and/or queues on different types of transportation facilities

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Computer Engineering; Computer Science; Data Science/Analytics; Software Engineering

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Computer Modeling/Simulation; Computer Science; Data Analysis; Data Science; Data Visualization; Software Development

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Toth, Andrew J

Participant Classifications: Undergraduate Student; Graduate Student—Master’s

Project Description:

This summer project will explore how human data can provide useful grounding for agentic artificial intelligence (AI) systems. Agentic AI systems can plan, reason, use tools, and take actions in support of human users; however, their effectiveness often depends on understanding the user’s current situation, environment, goals, attention, and intent. This project will investigate how data generated by or collected from people—such as gaze, gestures, speech, text, movement, physiological signals, first-person video, and interactions with the environment—can improve an agent’s contextual awareness.

The student will identify representative use cases and develop prototype agent workflows that incorporate one or more human-sensed data streams. The project will assess how these inputs can help an AI agent interpret user needs, ground language or task instructions in the surrounding environment, and provide more timely and relevant assistance.

Expected deliverables include a use-case assessment, prototype demonstration, and recommendations for future context-aware agent development.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: ARL West - Los Angeles, CA, On-site; Remote; Hybrid

Required Major(s):  Computer Science; Data Science/Analytics; Human Factors/Human Systems; Mathematics; Robotics/Autonomous Systems; Software Engineering; Psychology; Neuroscience; Any Major With the Required Skills

Preferred Technical Skills: Advanced Sensing; Augmented Reality/Virtual Reality; Data Analysis; Data Science; Human Factors Engineering/Human Systems Integration; Natural Language Processing; Computer Science; Computer Modeling/Simulation. Python, MatLab, 3D data analysis and modeling

Minumum Education Requirement: Graduate Student - Master's

Mentor Name:  Cohen Hoffing, Russell A

Participant Classifications: Graduate Student—Doctoral; Graduate Student—Master’s; Postdoctoral Researcher; College or University Faculty

Project Description:

In the future, the US Army will need to fuse information across a highly complex network of sensors, uncrewed aerial and ground vehicles (UAVs/UGVs), and wearable devices.  Therefore, data sources include multiple modalities (e.g., acoustic, visual, seismic), multiple vantage points, and must be processed as close to the edge as possible.

Successful applicants will collaborate with DEVCOM ARL researchers to create and/or implement approaches to solving this family of problems in theory, simulation, and/or experiments. Expertise in Python, JavaScript, Java, Docker, and Kubernetes will be useful for some projects.

Expected Deliverables
• Evaluated software prototypes and simulation models.
• Technical reports outlining algorithmic performance and scalability for publication at tier 1 conferences.
• A presentation at the ARL Summer Student Symposium.

By participating in this project, you will help bridge the gap between abstract distributed systems theory and real-world, edge-native resilience, directly advancing the foundational science of tactical cyber-physical systems.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site; Hybrid

Required Major(s): Computer Engineering; Computer Science; Data Science/Analytics; Electrical Engineering; Information Science/Technology; Mathematics; Mechanical Engineering; Robotics/Autonomous Systems; Any Major With the Required Skills

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Computer Science; Data Analysis; Data Science; Data Visualization; Mechanical Engineering; Software Development; Statistics; UI/UX Design; Web/Application Development. Expertise in Python, JavaScript, Java, Docker, ROS2, and Kubernetes will be useful for some projects.

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Twigg, Jeffrey N

Participant Classifications: Undergraduate Student; USMA Cadet; ROTC Cadet; Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; College or University Faculty

Project Description:

Multi-agent/Multi-robot systems operate in increasingly sophisticated, contested electromagnetic (EM) environments where these systems are easily detected and localized. Simultaneously, these environments are physically complex and difficult to autonomously navigate. The objective of this project is to develop a novel set of approaches that jointly optimize radio-frequency transmissions, retransmissions (through strategically placed reconfigurable intelligent surfaces), and maneuver trajectories of multi-agent systems to reduce detectable signature.

Participants will contribute to designing control policies that model the complex, multi-agent battlefield as a cooperative-competitive game-theoretic problem. Rather than relying solely on heuristic-based planning and/or reinforcement learning, the work may also focus on utilizing advanced Game Theory to solve for mathematically optimal policies that guarantee mission performance. Participants will develop algorithms for autonomous problem decomposition and explore how strategic relaxations of spatial-temporal mission.

Expected Deliverables
• Evaluated software prototypes and simulation models.
• Technical reports outlining algorithmic performance and scalability for publication at tier 1 conferences.
• A presentation at the ARL Summer Student Symposium.

By participating in this project, you will help bridge the gap between abstract distributed systems theory and real-world, edge-native resilience, directly advancing the foundational science of tactical cyber-physical systems.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site; Hybrid

Required Major(s): Aerospace/Aeronautical Engineering; Computer Engineering; Computer Science; Cybersecurity; Data Science/Analytics; Electrical Engineering; Information Science/Technology; Mathematics; Mechanical Engineering; Physics; Robotics/Autonomous Systems; Software Engineering; Statistics; Any Major With the Required Skills

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Computer Modeling/Simulation; Communication and Networking Technologies; Computer Science; Cybersecurity; Data Analysis; Data Science; Electrical Engineering; Mathematics; Mechanical Engineering; Physics; Software Development; Statistics. Python, MATLAB, C++, python, ROS2, Unity, Docker

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Twigg, Jeffrey N

Participant Classifications: Undergraduate Student; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher

Project Description:

Autonomous robots expected to operate in complex, unstructured, and contested environments must be equipped with adaptation capabilities. Current systems are limited in the tasks they can perform and the environments they can perform them in. The objective of this project is to research techniques that allow autonomous systems to rapidly adapt to new tasks or environments. Participants will engage in a thorough literature review of current state-of-the art, and then design, implement, and test techniques to enable robot adaptation. Research focus areas could include architectural adaptation that re-uses system components and training paradigms developed for generalist internet agents or interactive adaptation that uses human feedback as training signals. An overarching theme of the research will focus on adaptation solutions that are sample efficient. Adaptation applications may be explored for single agents or multi-agent teams. Solutions will be tested in simulation environments to evaluate performance relative to baseline techniques. Research methodology and evaluation results will be written up as a formal research paper targeting publication at a robotics conference and will support advancement of adaptation capabilities required to maintain resiliency during autonomous operation.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: ARL West - Los Angeles, C, Hybrid

Required Major(s): Computer Science; Robotics/Autonomous Systems

Preferred Technical Skills:  Artificial Intelligence/Machine Learning; Computer Science; Software Development. Experience in python is desired.

Minumum Education Requirement: Graduate Student - Master's

Mentor Name:  Wigness, Maggie B

Participant Classifications: Graduate Student—Doctoral; Graduate Student—Master’s; Postdoctoral Researcher

Project Description:

Active visual search (AVS) refers to the task of searching for a set of objects or regions of interest by changing pan, tilt, zoom (PTZ), and focal length of a PTZ camera to better recognize each object of interest. This is an activity done by people all the time, especially when they have access to binoculars yet this remains a difficult task for AI algorithms. The focus of the summer project will be to introduce new capabilities to an existing active visual search system. The focus will be either to improve the scene understanding of the system beyond object recognition (integrating depth estimation, object tracking, or visual segmentation models). Alternatively, based on interest the project may focus on extending the existing system to control PTZ security cameras over ROS2 along with using a visual language model to execute known human visual search methods.

Required skill set: Willingness to learn new things, experience with python or C++.

Requested Skill Sets: Experience machine learning, computer vision, ROS2, OpenCV, or prompt engineering with visual language models  is beneficial but not required. Some basic electronic hardware experience can be helpful if you wish to use a physical PTZ camera.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Aerospace/Aeronautical Engineering; Computer Engineering; Computer Science; Data Science/Analytics; Electrical Engineering; Information Science/Technology; Mechanical Engineering; Robotics/Autonomous Systems; Software Engineering

Preferred Technical Skills: Advanced Sensing; Artificial Intelligence/Machine Learning; Computer Science; Data Science; Electrical Engineering; Electronics; Mechanical Engineering

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Harrison, Andre V

Participant Classifications: Undergraduate Student; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; College or University Faculty

Project Description:

Many advanced AI algorithms require simulated environments to develop, test, and validate the performance and behavior of AI algorithms, especially those that interact or move within the surrounding environment. DEVCOM ARL has developed a simulator focused on testing and evaluating control methods for pan/tilt/zoom (PTZ) cameras. This summer project will focus on enhancing the metrics the simulator can calculate as well as extending the simulation capabilities. Interested interns may also work on porting the existing simulation capabilities to alternative simulation engines (Unreal, or Nvidia Omniverse). The aim at the end of the summer will be to either improve the qualitative of performance measures that can be used to evaluate control policies for PTZ cameras during object search or to increase the variety and variability of test, development and evaluation environments to evaluate these control policy methods.

Required skill Set: Willingness to learn new things.
Requested Skill Sets: Experience with C++/C (strongly preferred), experience with python is helpful, computer vision, unity or some other game engine development, ROS or opencv will be beneficial.

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Aerospace/Aeronautical Engineering; Computer Science; Information Science/Technology; Robotics/Autonomous Systems; Software Engineering; Any Major With the Required Skills

Preferred Technical Skills: Advanced Sensing; Artificial Intelligence/Machine Learning; Augmented Reality/Virtual Reality; Computer Modeling/Simulation; Computer Science; Data Visualization; Software Development

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Harrison, Andre V

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; USMA Cadet; ROTC Cadet; Graduate Student—Doctoral; Graduate Student—Master’s; Postdoctoral Researcher

Project Description:

Spiking Neural Networks (SNN) are the third generation of neural networks and are one potential solution to improving the energy efficiency future AI systems and to putting advanced learning algorithms on low power edge devices. Due to how they operate it is more appropriate to train SNN models on datasets with dynamically changing data rather than static data, but they have primarily been trained on static datasets. MNIST and EMNIST are handwritten number and character recognition datasets, respectively that have been central to the early development of machine learning algorithms, but they are static datasets displaying only the final result of a character or number after it has already been fully written. While stylus handwriting datasets are dynamic as they record the pen motions used to write characters and numbers. The aim of this project will be to create a synthesized stylus MNIST and EMNIST dataset to explore the testing and training of character classification algorithms.

Existing stylus datasets can be used to make an initial approximation of the stylus EMNIST dataset, but additionally stylus samples may need to be synthesized using generative models such as (Kotani et al. ECCV 2020) or modern GenAI methods such as diffusion or flow matching methods. The quality of the reconstruction can be evaluated against the original datasets. The use of SNN models for the classification task is encouraged, but not required.

Required skill set: Willingness to learn new things. Experience with Python or C++/C
Requested Skill Sets: Experience with machine learning, computer vision, deep neural networks, GenAI, optical character recognition, or basic computational neuroscience is beneficial

Army Research Directorate (ARD) Division: Information, Autonomy & Teaming Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Computer Science; Data Science/Analytics; Information Science/Technology; Mathematics; Neuroscience; Robotics/Autonomous Systems; Electrical Engineering; Any Major With the Required Skills

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Computer Science; Data Science; Electrical Engineering; Natural Language Processing; Software Development

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Harrison, Andre V

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; College or University Faculty

Project Description:

This opportunity seeks methods to efficiently deploy CNNs, vision and multimodal transformers, and language models on resource-constrained tactical-edge platforms. The goal is to overcome the power, memory, thermal, and compute limits of deployed hardware, enabling low-latency, on-device inference and reasoning in austere or communications constrained environments. Topics include edge-computing methods, hardware–software co-design, neural architecture search, neuromorphic systems, and optimized embedded accelerators such as GPUs, NPUs, and FPGAs. Proposed approaches should improve model efficiency, accuracy, robustness, scalability, and inference management through advances in optimization, compression, abstraction layers, and deployment frameworks. Priority areas also include multimodal sensor fusion using unified transformer encoders and model-specific improvements, such as CNN feature extraction and transfer learning, as well as LLM and small-transformer prompt optimization, domain adaptation, continual learning, contextual reasoning, and efficient inference. Solutions should support U.S. Army missions including target detection, anomaly and threat assessment, autonomous navigation, and human–machine teaming.

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Computer Science; Computer Engineering; Data Science/Analytics; Information Science/Technology; Mathematics; Neuroscience; Software Engineering; Robotics/Autonomous Systems; Statistics

Preferred Technical Skills: Advanced Sensing; Artificial Intelligence/Machine Learning; Computer Modeling/Simulation; Computer Science; Mathematics; Natural Language Processing; Statistics; UI/UX Design; Web/Application Development. Python programming. Neural Network Model training

Minumum Education Requirement:Graduate Student - Master's

Mentor Name:  Dasari, Venkateswara R

Participant Classifications: Postdoctoral Researcher; Graduate Student—Doctoral; Graduate Student—Master’s

Project Description:

This project investigates how deep learning can support real time radio frequency (RF) signal classification on resource constrained edge devices. As wireless environments grow more complex, quickly recognizing signal types is important for effective spectrum awareness. Traditional signal processing methods can be too slow or computationally intensive for small, low power platforms.

The objective of this internship project is to explore lightweight deep learning approaches that could eventually be deployed at the edge for basic RF signal identification. The participant will work with existing RF datasets, perform data preprocessing, and experiment with compact neural network models suitable for limited hardware. Tools may include Python, PyTorch, and simple embedded AI frameworks.

The work will focus on training and evaluating selected models rather than developing full production systems. The intern will compare accuracy and efficiency across approaches and document challenges that arise when applying deep learning to RF signals.

The expected outcome is a preliminary prototype model and a short evaluation summary describing its performance and potential next steps. This project provides hands on experience with applying AI methods to RF data while keeping the scope manageable within a 10 week timeframe.

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Computer Engineering; Computer Science; Electrical Engineering; Information Science/Technology

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Communication and Networking Technologies; Computer Science; Electrical Engineering. Candidate skills that would help in this role include:
- Strong background in machine learning or deep learning, and proficiency in Python and common ML frameworks (such as PyTorch or TensorFlow).
- Experience with RF signal processing, wireless communications, or electromagnetic spectrum analysis.
- Familiarity with model optimization techniques (quantization, pruning, knowledge distillation, etc.).
- Experience working with edge or embedded computing platforms and understanding SWaP constraints
- Strong experimental, evaluation, and debugging skills for complex AI systems.

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Wang, Peng

Participant Classifications: Undergraduate Student; USMA Cadet; ROTC Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; College or University Faculty

Project Description:

Modern cyber-physical systems operate in environments where adversarial interactions span interconnected domains, including cyber networks and physical processes. Actions in one domain influence observability, capabilities, and outcomes in others, creating complex strategic dependencies. Deception plays a central role, not only through explicit mechanisms such as decoys or honeypots, but also through strategic behavior that shapes an adversary’s beliefs and decisions over time.

This project proposes a foundational study of multi-domain deception through the lens of game theory, and learning. The goal is to develop principled models and scalable solution methods for designing and evaluating strategies in adversarial environments with partial and imperfect information. We aim to develop a general framework that captures belief manipulation, cross-domain interactions, and adaptive strategic behavior.
The potential research thrusts are: (1) modeling deception as belief shaping under incomplete information games, (2) integrating learning-based methods for adaptive and data-driven deception, and (3) developing scalable algorithms for large, structured multi-domain systems.

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site; Hybrid

Required Major(s):  Computer Engineering; Computer Science; Cybersecurity; Data Science/Analytics; Electrical Engineering; Information Science/Technology; Mathematics; Software Engineering; Statistics

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Communication and Networking Technologies; Computer Modeling/Simulation; Computer Science; Cybersecurity; Data Analysis; Data Science; Data Visualization; Mathematics; Natural Language Processing; Statistics; Software Development, Game theory

Minumum Education Requirement: Graduate Student - Master's

Mentor Name:  Kamhoua, Charles A

Participant Classifications: Graduate Student—Doctoral; Graduate Student—Master’s; Postdoctoral Researcher

Project Description:

SNAP is an optical fiber technology boasting ultralow loss and exceptional subangstrom fabrication precision, making it a promising platform for light control in quantum networks. The participant will explore novel approaches for creating SNAP systems and for coupling light into and out of them.

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site; Hybrid; Remote

Required Major(s): Electrical Engineering; Physics; Optics

Preferred Technical Skills: Communication and Networking Technologies; Physics; Useful skills: Experimental optics experience, MATLAB, spectroscopy, understanding of waveguide optical modes or optical resonators

Minumum Education Requirement: Recent Undergraduate Student

Mentor Name: Vitullo, Dashiell L

Participant Classifications: Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; College or University Faculty; Recent Undergraduate Graduate

Project Description:

The concept of reconfigurable intelligent surfaces (RISs) has been the subject of intensive studies in recent years. Their ability to reflect/re-transmit incoming electromagnetic (EM) waves to desired director offers new approach to applications such as radar imaging and sensing, wireless communication, or the combination of both concepts, integrated sensing and communication (ISAC). In this topic, we focus on designing RIS to control or manipulate the target doppler signature. We will incorporate the use of PIN diode or varactor into the surface to create the controllable phase profile generated across the elements. This will allow us to control reflect/transmit EM waves with respect to time, which will be the key element to control the target doppler signature.

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Computer Engineering; Electrical Engineering; Materials Science/Engineering

Preferred Technical Skills: Advanced Sensing; Artificial Intelligence/Machine Learning; Electrical Engineering; Electronics

Minumum Education Requirement: Graduate Student - Master's

Mentor Name:  Nguyen, Quang M

Participant Classifications: Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher

Project Description:

Distributed networks and edge sensor arrays operate under limited communication bandwidth, power, and computing capacity. Continuously processing and transmitting sensor data can consume scarce resources, while sharing too little can degrade estimation accuracy. How can collaborating agents determine which measurements are worth acquiring, processing, and communicating? This project will investigate resource-allocation policies guided by the expected reduction in decision-relevant uncertainty. The objective is to determine when selectively sensing, processing, and sharing information can maintain estimation accuracy while reducing resource use.

Working with technical mentors, the student will build or extend a modular simulation in which multiple agents use different sensors to track a moving source under communication and computing constraints. The student will implement baseline tracking filters and fixed-schedule policies, develop uncertainty-guided alternatives, and compare their performance across varying resource budgets, communication dropouts, and node failures. Evaluation will examine tracking error, communication volume, computational cost, and whether reported uncertainty accurately reflects estimation errors. A key outcome will be understanding when adaptive coordination improves performance and when it introduces delay, redundancy, or overconfidence.

Anticipated tools include Python, scientific computing libraries, and Bayesian estimation methods such as extended Kalman filtering. Expected deliverables include a simulation testbed, benchmark results comparing adaptive and fixed-schedule policies under comparable resource constraints, and a technical report. Results may also support a conference publication. Helpful background includes Python programming experience and familiarity with one or more of the following: distributed systems, estimation or control theory, machine learning, and networking.

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Computer Engineering; Computer Science; Aerospace/Aeronautical Engineering; Data Science/Analytics; Electrical Engineering; Robotics/Autonomous Systems; Software Engineering; Statistics

Preferred Technical Skills:  Advanced Sensing; Artificial Intelligence/Machine Learning; Computer Modeling/Simulation; Computer Science; Data Science; Electrical Engineering; Mathematics. Helpful background includes Python programming experience and familiarity with one or more of the following: distributed systems, estimation or control theory, machine learning, and networking.

Minumum Education Requirement: Graduate Student - Master's

Mentor Name:  Haile, Mulugeta A

Participant Classifications: Graduate Student—Doctoral; Graduate Student—Master’s; USMA Cadet; College or University Faculty; ROTC Cadet; Postdoctoral Researcher

Project Description:

Researchers developing AI/ML methods for distributed radio-frequency (RF) sensing need diverse, accurately labeled datasets to train and evaluate detection, localization, and tracking models. Experimental data collection is costly and time-consuming, often covers limited receiver geometries and propagation conditions, and may lack precise ground truth. These gaps make it difficult to assess whether models will perform reliably in new environments. This project addresses this challenge by developing and verifying a reusable simulation workflow that balances physical fidelity and computational cost. Using electromagnetic solvers such as COMSOL Multiphysics with its RF Module or Ansys HFSS alongside ray-tracing tools - where appropriate, the workflow will generate synthetic sensor data across varied environments and configurations. The ideal outcome is a documented simulation testbed that produces customizable, labeled datasets with known ground truth within each simulated scenario, supporting model training and systematic performance evaluation.

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Aberdeen Proving Ground, MD, Hybrid

Required Major(s): n/a

Preferred Technical Skills: Aerospace Engineering; Advanced Sensing; Artificial Intelligence/Machine Learning; Augmented Reality/Virtual Reality; Electrical Engineering; Mechanical Engineering; Mathematics; Rapid Prototyping/Additive Manufacturing; Computer Modeling/Simulation; Electronics; Energy Technologies; Physics; Statistics; Useful skill sets: Experience generating synthetic data using simulation tools such as COMSOL Multiphysics with its RF Module, Ansys HFSS, Abaqus, or similar software. Familiarity with ray-tracing tools is also helpful.

Minumum Education Requirement: Recent Graduate - Master's

Mentor Name:  Haile, Mulugeta A

Participant Classifications: Graduate Student—Doctoral; Graduate Student—Master’s; USMA Cadet; College or University Faculty; ROTC Cadet; Postdoctoral Researcher

Project Description:

Modern cybersecurity analysts face an unsustainable volume of high-dimensional telemetry and alert fatigue, creating critical operational delays. This project addresses the core research question: How can advanced artificial intelligence be systematically integrated into cyber defensive workflows to automate routine triage, reduce analytical friction, and optimize response posture without compromising operational security? The primary objective is to design, implement, and benchmark an intelligent automation framework that ingests heterogeneous security logs, applies predictive modeling to prioritize threats, and streamlines critical analytical workflows for defensive operators.

The participant will actively investigate workflow bottlenecks, design robust data processing pipelines, and train task-specific machine learning models for anomaly detection, automated triage, and incident summarization. Utilizing methods and tools including Python, modern ML/LLM frameworks, SIEM/SOAR interfaces, and containerization platforms, the participant will integrate algorithmic decision support into live or simulated environments. The expected deliverables include a functional software prototype, a comprehensive technical evaluation report detailing system latency and threat triage efficacy, and deployment-ready documentation for mission stakeholders.

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: ARL South - Austin, TX,On-site; Remote; Hybrid

Required Major(s): Computer Science; Data Science/Analytics; Information Science/Technology; Any Major With the Required Skills

Preferred Technical Skills: Computer Science; Cybersecurity; Data Analysis; Data Science; Artificial Intelligence/Machine Learning; python, agentic ai, experience with frontier and open weights providers

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Acosta, Jaime C

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral

Project Description:

The U.S. DEVCOM Army Research Laboratory (ARL) is pleased to announce an exciting opportunity for a summer internship with a focus on microsystems, ferroelectrics, 2.5D integration and MEMS. Responsibilities will include circuit design, chip to chip bonding, novel material fabrication, and testing. The successful candidate will primarily be stationed in Adelphi, Maryland.

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site; Hybrid

Required Major(s): Biomedical Engineering; Chemical Engineering; Electrical Engineering; Materials Science/Engineering; Mechanical Engineering; Physics; Any Major With the Required Skills

Preferred Technical Skills: Advanced Sensing; Electrical Engineering; Electronics; Energy Technologies; Materials Engineering; Mechanical Engineering; Physics
• Pursing a degree in Electrical Engineering, Mechanical Engineering, Materials Science, Physics, or a related field.
• Experience with electrical testing, breadboarding, IC packaging, and MEMS
• Effective communication skills, both written and verbal.
• Ability to work in a team and collaborate with researchers from diverse backgrounds.
• US citizenship with the ability to hold a security clearance.
• Experience with microfabrication processes, specific to MEMS.
• Experience with IC packaging, chip to chip bonding and multichip packaging techniques
• Experience with IC design, basic circuitry, and SPICE tools.
• Experience with MEMS applications and testing.

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Coleman-Walter, Kathleen P

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral

Project Description:

In IOT-class sensor systems, sensor networks are often performance-constrained due to the size & power of the platforms. With multi-modal sensing modalities, there requires some level of processing to understand the various sensor signals and characterize the events. This study aims to address this need by developing AIML algorithms and custom digital-signal processing to be implemented on a smartwatch-class microprocessor and/or Single board computer i.e. Raspberry Pi. The intern will implement low-level software code (C, C++) and test basic signal processing & AIML algorithms on commercial, low-power MCUs. The goal would be to quantify the performance of well-established algorithms (CNN, Random Forest, KNN, etc.) on 32-bit processors for power efficiency, accuracy, reliability, etc. then integrate the ‘best’ AIML algorithm for demonstration on current sensor system(s).  

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Computer Engineering; Computer Science; Software Engineering

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Computer Science; Computer Modeling/Simulation; Data Analysis; Data Science; Software Development; Statistics; Python, Yocto, C, C++

Minumum Education Requirement: Undergraduate Student

Mentor Name: Heintzelman, S M

Participant Classifications: Recent Undergraduate Graduate; Undergraduate Student; Graduate Student—Master’s; Postdoctoral Researcher

Project Description:

Nitrogen vacancy centers in diamond offer an interesting platform for electromagnetic sensing. An opportunity exists for summer researchers to experimentally investigate the fundamental science of NV-based electromagnetic sensors and their integration with photonic integrated circuits. This research will involve modeling, measurement, analysis and design refinement, focused on understanding fundamental and practical performance limitations and the best ways to overcome them.

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Electrical Engineering; Physics; Any Major With the Required Skills

Preferred Technical Skills: Advanced Sensing; Physics; Prior experimental experience with NV centers or photonic integrated circuits will be looked upon favorably.

Minumum Education Requirement: Graduate Student - Doctoral

Mentor Name: Vitullo, Dashiell L

Participant Classifications: Graduate Student—Doctoral; Postdoctoral Researcher

Project Description:

Support the development, implementation, and testing of RF hardware and/or antenna systems for signal analysis and detection. Depending on skill level and prior experience, participant may develop antenna simulations in FEKO, perform bench testing of RF hardware, and execute in situ prototype testing. Experience with MATLAB or Python and some knowledge of RF systems is preferred.

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Computer Engineering; Electrical Engineering; Physics

Preferred Technical Skills: Electrical Engineering; Electronics

Minumum Education Requirement: Undergraduate Student

Mentor Name: Bouvy, Alex A

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher

Project Description:

Support embedded systems design for RF detector systems that implement spectrum sensing and direction finding capabilities. Depending on skill level and prior experience, participant may develop embedded code on single board computers to interface with external peripherals, code for software defined radios, and perform some RF testing. Experience with Python and some knowledge of RF is preferred.

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Computer Engineering; Electrical Engineering; Physics

Preferred Technical Skills: Communication and Networking Technologies; Electrical Engineering

Minumum Education Requirement: Undergraduate Student

Mentor Name:  Bouvy, Alex A

Participant Classifications: Undergraduate Student; Graduate Student—Doctoral; Graduate Student—Master’s; Postdoctoral Researcher

Project Description:

Multiple summer internship opportunities are available for undergraduate and graduate students in electrical engineering, computer science, and related disciplines to support research in wireless networking and sensing. Interns will join multidisciplinary teams advancing self-managing, resilient, and intelligent wireless networks and will collaborate with subject-matter experts in physical-layer technologies, network optimization, signal processing, applied machine learning, edge artificial intelligence, and wireless communications experimentation. As a leader in the fundamental science and application of communications technologies, ARL offers access to state-of-the-art experimental facilities and high-performance computing platforms to enable efficient execution of impactful research projects.

POCs: Dr. Fikadu Dagefu (Fikadu.t.dagefu.civ@army.mil and Dr. Kevin Chan (kevin.s.chan.civ@army.mil)

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Computer Science; Computer Engineering; Electrical Engineering; Data Science/Analytics; Cybersecurity; Mathematics; Robotics/Autonomous Systems

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Communication and Networking Technologies; Computer Modeling/Simulation; Computer Science; Cybersecurity; Data Analysis; Data Science; Electrical Engineering; Mathematics; Physics

Minumum Education Requirement: Undergraduate Student

Mentor Name: Dagefu, Fikadu T

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral

Project Description:

Ultra-Wideband scanning over upper hemispherical coverage is used to identify possible signals that may be correlated. Special algorithms are used for the signal locations and signal contents. Fast comparisons and correlation analysis between different signals is also performed, also using special algorithms. The results of the analysis will show the correlations between the different signals at different frequencies and at different locations over the hemispherical coverage. 

Army Research Directorate (ARD) Division: Network, Electronic Warfare & Cyber Sciences Division

Research Location: Adelphi Laboratory Center, MD, Hybrid

Required Major(s):  Computer Engineering; Data Science/Analytics; Electrical Engineering; Information Science/Technology; Mathematics; Statistics

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Communication and Networking Technologies; Data Analysis; Data Science; Electrical Engineering; Mathematics; Physics; Software Development; Statistics; MATLAB, FEKO, GRASP. Signal Processing, Neural Networks, AI

Minumum Education Requirement: Undergraduate Student

Mentor Name: Zaghloul, Amir I

Participant Classifications: Undergraduate Student; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; College or University Faculty

Project Description:

The successful intern would aid in the synthesis, purification, and characterization of new organic and organometallic chromophores for the development of novel non-linear optical (NLO) materials. The intern would gain experience in analyzing chemical characterization techniques such as nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), x-ray crystallography, electrochemistry, and optical characterization including, UV-visible absorption, photoluminescence, transient absorption, and Z-scan spectroscopies. References: [1]Olumba, M. E., et al., Inorg. Chem. 2022, 61, 48, 19344–19353; [2] O'Donnell, R. M., et al., Inorganic Chemistry 2017, 56 (15), 9273-9280. Background in synthetic chemistry or chemical characterization NMR spectroscopy, mass spectrometry, x-ray crystallography, and electrochemistry), or optical characterization (UV-visible absorption, photoluminescence, transient absorption, and Z-scan) is preferred but not required

Army Research Directorate (ARD) Division: Power & Energy Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Chemistry; Chemical Engineering; Physics; Materials Science/Engineering

Preferred Technical Skills:  Chemical Sciences/Engineering; Physics

Minumum Education Requirement: Undergraduate Student

Mentor Name: Rohrabaugh, Thomas N Jr

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; USMA Cadet; ROTC Cadet; Graduate Student—Doctoral; Graduate Student—Master’s; Postdoctoral Researcher; College or University Faculty

Project Description:

The successful Faculty/Cadet would aid in the measurements and characterizations and/or simulations of the the interaction between high power continuous wave (CW) lasers and various materials, possibly including optics, structural materials, fabrics, etc.  Measurements may include laser-induced damage threshold (LIDT) of various optical materials of relevance to Army systems. Simulations will focus on the thermal effects and factor in time, spot size, beam distribution, power, etc.  Measurements will be based on ISO Laser Damage Standards 21254. The Faculty/Cadet would gain experience in high power continuous wave laser operation, laser damage measurements of optical materials, characterization of optical materials and thermal simulations of optics under cw laser exposure.  The exact scope of the AIAD would be tailored to the Faculty/Cadet’s background and time constraints.

Army Research Directorate (ARD) Division: Power & Energy Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site; Remote; Hybrid

Required Major(s): Physics; Any Major With the Required Skills; Materials Science/Engineering; Experience with lasers and/or optics would be beneficial.

Preferred Technical Skills: Physics; For the experimental aspect, experience with lasers and/or optics would be highly beneficial.  Experience with high power continuous wave lasers would be ideal, but not necessary.

For the simulation aspect, experience with COMSOL Multiphysics would be beneficial. 

This opportunity can be solely simulation, solely experimental, a hybrid of both, or start from one and learn and develop the skills for the other.  

Minumum Education Requirement: Undergraduate Student

Mentor Name: McElhenny, John E

Participant Classifications: USMA Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; College or University Faculty; Recent Undergraduate Graduate; Undergraduate Student

Project Description:

Standard lithium-ion batteries—like the ones in smartphones and electric vehicles—suffer severe performance drops when temperatures plummet. In freezing environments, the liquid electrolyte inside the battery thickens, slowing down the movement of lithium ions and causing rapid power loss. This summer project focuses on developing next-generation battery electrolytes engineered to maintain high conductivity and stability under sub-zero conditions. The intern(s) will learn to use machine learning algorithms to screen and optimize electrolyte compositions. The project will introduce the student(s) to advanced lithium-ion battery concepts, physicochemical measurements, electrochemistry, and design of experiments methodologies.

Army Research Directorate (ARD) Division: Power & Energy Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Chemical Engineering; Chemistry; Data Science/Analytics; Materials Science/Engineering; Physics; Statistics; Mechanical Engineering

Preferred Technical Skills:  Chemical Sciences/Engineering; Data Analysis; Data Visualization; Energy Technologies

Minumum Education Requirement: Undergraduate Student

Mentor Name: Pastel, Glenn R

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; USMA Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher

Project Description:

1. Vision and Motivation
   As the global transition toward electrification, autonomous systems, and data-intensive artificial intelligence accelerates, the performance boundaries of modern electronic devices are increasingly defined not by the silicon switch alone, but by the packaging architecture that encloses it. Contemporary applications demand the simultaneous co-existence of high-voltage (HV), high-power density, high-frequency, and optical routing within tightly integrated modules. Conventional packaging solutions—traditionally siloed into separate electrical, thermal, and optical domains—suffer from catastrophic bottlenecks, including parasitic inductances, thermal runaways, signal attenuation, and optical misalignment.
   The vision of this research program is to pioneer a unified, multi-physics framework for advanced electronic packaging that seamlessly integrates high-voltage insulation, high-power thermal management, high-frequency signal integrity, and potentially co-packaged optics. By replacing traditional packaging paradigms with advanced materials, 3D heterogenous integration, and holistic co-design methodologies, this research will unlock unprecedented power densities, operational speeds, and reliability for next-generation power electronics, aerospace systems, and data-center optical interconnects.
2. Core Research Objectives
   High-Voltage and High-Power Thermal-Mechanical Co-Design:
   Develop advanced wide-bandgap (e.g.,"SiC" or "GaN" ) power module packaging utilizing high-thermal-conductivity ceramics and active double-sided liquid cooling.
   Investigate novel high-dielectric-strength encapsulants and partial-discharge-resistant insulation materials to suppress electric field concentrations and prevent breakdown under extreme high-voltage transient regimes. Compare traditional methods vs. additive manufacturing methods.
   Investigate methods for die attach and wire-bonding techniques for newly developed high-voltage devices (e.g. MOSFETS, IGBTs, diodes).
   Computer-Aided Design (CAD) electronic packaging layout utilizing co-engineering/co-design methods.
   Develop high-voltage, high-power, high-frequency, and optical instrumentation techniques (sometimes outside of their intended mode of operation) to validate and quantify success of packaging exercise. 
3. Methodological Approach
   Advanced Simulation & Co-Design: Employ a suite of Electronic Design Automation (EDA) and multi-physics tools to co-optimize electrical, thermal, and mechanical signal paths concurrently.
   Cleanroom Fabrication & Packaging Prototyping: Utilize advanced manufacturing processes—including active metal braze (AMB) substrates, direct bonded copper (DBC), low-temperature co-fired ceramics (LTCC), wafer-level hybrid bonding, and potentially precision laser alignment for photonics.
   Extreme-Environment Characterization: Subject fabricated modules to rigorous multi-stress testbeds, combining high-voltage partial discharge testing, high-frequency network analyzer measurements, thermal cycling (-55°C to 175°C), and high-power operational burn-in to validate long-term reliability and failure mechanisms.
4. Broader Impacts and Applications
   The realization of this multi-domain advanced packaging framework will catalyze breakthroughs across critical infrastructure sectors:
   Next-Generation Electric Mobility, medical devices, and Renewable Energy, : Enabling high-voltage, high-frequency "SiC" power modules for next-gen electric vehicle (EV) traction inverters, ultra-compact medical devices, and smart-grid solid-state transformers with drastically reduced volume and weight.
   Hyperscale Data Centers & AI Clusters: Deploying ultra-high-bandwidth, power-efficient optical engines and co-packaged optics that slash data center energy consumption and latency.
   Aerospace & Defense Systems: Delivering compact, radiation-hardened, and thermally robust RF/microwave and power electronics capable of surviving extreme environments.

Army Research Directorate (ARD) Division: Power & Energy Sciences Division

Research Location: Adelphi Laboratory Center, MD, On-site

Required Major(s): Mechanical Engineering; Electrical Engineering; Materials Science/Engineering; Chemical Engineering; Aerospace/Aeronautical Engineering

Preferred Technical Skills: Aerospace Engineering; Chemical Sciences/Engineering; Computer Modeling/Simulation; Electrical Engineering; Electronics; Materials Engineering; Mechanical Engineering; Rapid Prototyping/Additive Manufacturing; SolidWorks knowledge preferred, but not required.

Minumum Education Requirement: Undergraduate Student

Mentor Name: Hinojosa, Miguel

Participant Classifications: High School Student; Recent Undergraduate Graduate; Undergraduate Student; USMA Cadet; ROTC Cadet; Graduate Student—Doctoral; Graduate Student—Master’s; Postdoctoral Researcher; K–12 Teacher; College or University Faculty

Project Description:

This position involves developing experimental procedures, analysis techniques, and advanced modeling approaches in a greater effort to measure, understand, or predict the biomechanics of biological tissue in high-rate impact scenarios. The work performed in this position will support a larger effort to improve computational human body models designed for simulating impact events by contributing to more biofidelic constituent materials and models and reproducing more realistic loading conditions.

Army Research Directorate (ARD) Division: Terminal Effects Division

Research Location: Aberdeen Proving Ground, MD,Hybrid; Remote; On-site

Required Major(s):  Aerospace/Aeronautical Engineering; Biomedical Engineering; Computer Science; Data Science/Analytics; Materials Science/Engineering; Mechanical Engineering; Neuroscience; Physics; Mathematics; Statistics; Software Engineering; Any Major With the Required Skills

Preferred Technical Skills: Mathematics; Mechanical Engineering; Physics; Data Analysis; Data Science; Data Visualization; Biological Sciences/Engineering; Statistics

Minumum Education Requirement: Undergraduate Student

Mentor Name: Rafaels, Karin A

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher

Project Description:

The project involves implementing a newly designed and constructed DEVCOM ARL hyperspectral imaging system as a novel diagnostic to understand the internal structure of the fireball produced by aluminized explosives. The investigation will focus on optimizing optical configurations of the system for expected fireball scenes. Students will calibrate and develop data processing pipelines while collecting data from energetic and combustion phenomena. Time permitting, advanced data processing/management routines will be established.

Army Research Directorate (ARD) Division: Weapons Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Materials Science/Engineering; Mechanical Engineering; Chemical Engineering; Physics; Chemistry; Computer Science

Preferred Technical Skills: Materials Engineering; Mechanical Engineering; Physics; Data Analysis; Computer Science; Data Visualization; high-speed videography, spectroscopy, pyrometry, energetic materials, reactive materials, optics, photonics

Minumum Education Requirement: Graduate Student - Master's

Mentor Name:  Wainwright, Elliot R

Participant Classifications: Graduate Student—Master’s; Graduate Student—Doctoral

Project Description:

Multiple nanosecond laser pulses, fired simultaneously, can be used to study the mixing of micro-shock waves at laboratory scales. This project involves an experiment designed to study this microshock mixing as a function of the laser energy and distance between shock origins via multi-flash schlieren imaging and high-fidelity microphones and/or novel PVDF gauges. This work will support the growing body of research demonstrating pulsed laser systems and applicable optical techniques as potential surrogates for much larger detonation experiments.

Army Research Directorate (ARD) Division: Weapons Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Materials Science/Engineering; Mechanical Engineering; Physics; Chemical Engineering

Preferred Technical Skills: Materials Engineering; Data Analysis; Computer Modeling/Simulation; Chemical Sciences/Engineering; Mechanical Engineering; Physics; Cameras, image analysis, data analysis, shock physics

Minumum Education Requirement: Graduate Student - Master's

Mentor Name: Wainwright, Elliot R

Participant Classifications: Graduate Student—Doctoral; Graduate Student—Master’s

Project Description:

This project involves investigations to the structural changes of novel metal alloys and organic materials. The project goals are as follows: high-pressure x-ray diffraction collection and structural refinement using diamond anvil pressure cells. Correlation of several data sets of different alloy compositions and organic structural motifs to understand the pressure response over elevated pressure conditions. Alloys and materials to be studied will be prepared in house. High pressure work involves micrograms of material and uses high-pressure techniques and synchrotron x-ray radiation.

Army Research Directorate (ARD) Division: Weapons Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s): Chemistry; Materials Science/Engineering; Physics

Preferred Technical Skills:  Artificial Intelligence/Machine Learning; Data Analysis; Data Visualization; Diamond anvil cell technique, x-ray powder diffraction reduction.

Minumum Education Requirement: Undergraduate Student

Mentor Name: Jenkins, Timothy A

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral; USMA Cadet

Project Description:

In support of experimental and theoretical efforts at ARL, the proposed project will investigate and apply bottom-up, hierarchical multiscale approaches that scale-bridge atomistic, molecular dynamics (MD) and/or microscale, coarse grain (CG) descriptions of energetic materials (EM) to inform mesoscale and macroscale models implemented within continuum codes. The multiscale framework will provide a fundamental understanding of the effects of microstructure on meso-scale phenomena crucial to the shock initiation and chemical reaction of heterogeneous EMs.

The intern’s role in this project will be to develop, implement, test and transition model enhancements to the multiscale modeling framework. The intern will be expected to complete several simulations using HPC computational resources, analyze the data generated, and present their work in oral and written forms. 

Army Research Directorate (ARD) Division: Weapons Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site; Hybrid; Remote

Required Major(s): Chemical Engineering; Chemistry; Computer Engineering; Computer Science; Materials Science/Engineering; Mechanical Engineering; Physics

Preferred Technical Skills: Artificial Intelligence/Machine Learning; Chemical Sciences/Engineering; Computer Modeling/Simulation; Computer Science; Data Analysis; Data Visualization; Materials Engineering; Mechanical Engineering; Physics. Experience in computational modeling and simulation, multiscale modeling, programming experience, LAMMPS.

Minumum Education Requirement: Graduate Student - Doctoral

Mentor Name:  Larentzos, James P

Participant Classifications: Graduate Student—Doctoral; Postdoctoral Researcher

Project Description:

The ARL Flight Sciences Branch Aerodynamic research area involves the study of aerodynamic flow phenomena of both guided and unguided munitions (e.g., projectiles and missiles) to determine how they affect aerodynamic forces and moments and aerodynamic heating. For both unguided and guided flight, research involves detailed computational analysis of wake flows, shock-boundary layer interactions, vortex interactions and vortex-shock interactions across subsonic, transonic, supersonic, and hypersonic flight regimes for both improved understanding of the physics and prediction accuracy. For guided flight, research involves investigation of control effectors, both traditional (fins, flaps, canards) and non-traditional (pins, bent noses), to increase maneuverability across flight regimes. Aerodynamic research makes significant use of computational fluid dynamic (CFD) tools including Euler, Reynolds-Averaged Navier-Stokes (RANS), hybrid RANS/Large Eddy Simulation (LES), and LES and leverages our on-site free-flight ranges for experimental validation, including heat transfer. Coupled CFD/six degree-of-freedom (6DOF) rigid body dynamics (RBD) models are also used, as needed, to understand the flight of complex projectiles.

The specific computational or experimental project will be based on a subset of the research described at a level appropriate to the applicant. A basic knowledge of high Reynolds number aerodynamics is preferred. Investigation of surrogate modeling as a rapid design tool has just begun and could also be an area of focus.

Army Research Directorate (ARD) Division: Weapons Sciences Division

Research Location:  Aberdeen Proving Ground, MD, On-site

Required Major(s):  Aerospace/Aeronautical Engineering; Mechanical Engineering

Preferred Technical Skills:  Aerospace Engineering; Computer Modeling/Simulation. Preferred skills: Computational Fluid Dynamics, Python or Matlab, high Reynolds number aerodynamics

Minumum Education Requirement: Undergraduate Student

Mentor Name: Silton, Sidra I

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; USMA Cadet; ROTC Cadet; Graduate Student—Master’s; Graduate Student—Doctoral; Postdoctoral Researcher; College or University Faculty

Project Description:

Research Problem: The rapid proliferation of small, low-altitude Uncrewed Aircraft Systems (UAS) presents unique security and airspace management challenges. Standard radar and surveillance systems often struggle to reliably detect, classify, and track these small, highly maneuverable platforms due to their low radar cross-section (RCS) and high ground clutter.

Project Objective: The objective of this project is to develop and evaluate sensor fusion and state estimation algorithms that combine heterogeneous sensor data to achieve robust, real-time tracking of non-cooperative UAS.

Participant Scope: The participant will model synthetic radar, optical, and acoustic sensor measurements of dynamic UAS flight paths. They will design and implement filtering algorithms to estimate aircraft states, handle missing data, and resolve measurement associations across multiple simulated sensor nodes.

Methods and Technologies: The student will utilize state estimation and tracking methodologies, such as Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), or Particle Filters. Development and simulation will be conducted using Python (NumPy, SciPy, FilterPy) or MATLAB, alongside open-source geospatial tools to model local terrain constraints.

Expected Deliverables: The project will yield a functional multi-sensor tracking simulation repository, a benchmark report evaluating tracking accuracy (e.g., Root Mean Square Error) under varying noise levels, and a final technical presentation detailing the performance of the implemented algorithms.

Army Research Directorate (ARD) Division: Weapons Sciences Division

Research Location: Aberdeen Proving Ground, MD, On-site

Required Major(s):  Aerospace/Aeronautical Engineering; Electrical Engineering; Mathematics; Mechanical Engineering; Physics; Robotics/Autonomous Systems; Any Major With the Required Skills

Preferred Technical Skills: Advanced Sensing; Aerospace Engineering; Artificial Intelligence/Machine Learning; Augmented Reality/Virtual Reality; Computer Modeling/Simulation; Computer Science; Electrical Engineering; Mathematics; Mechanical Engineering; Physics; Rapid Prototyping/Additive Manufacturing

Minumum Education Requirement: Undergraduate Student

Mentor Name: Maley, James M

Participant Classifications: Undergraduate Student; Recent Undergraduate Graduate; Graduate Student—Master’s; Graduate Student—Doctoral; College or University Faculty; Postdoctoral Researcher