DEVCOM Army Research Laboratory
Research Associateship Program (RAP)

Project Description:

The U.S. DEVCOM Army Research Laboratory (ARL) is pleased to announce an exciting opportunity for a research internship with a focus on Microsystems, ferroelectric based sensors, and MEMS. Responsibilities will include circuit design based on simple energy harvesting circuits, and analog circuits, rapid prototyping both 3d fixtures and PCB, sensor characterization and electrical testing. Collaborate and communicate findings with ARL engineers and scientists. The successful candidate will primarily be stationed in Adelphi, Maryland.

Research Location: Adelphi, MD, hybrid

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): Electrical Engineering, Mechanical Engineering, Materials Science, Physics, or a related field. 

Preferred Technical Skills: 

• Experience with microfabrication processes, specific to MEMS.
• Experience with IC design, basic circuitry, and SPICE tools.
• Experience with rapid prototyping including 3D printing and PCB.
• Experience with MEMS design, sensor development.
• Experience with COMSOL or relevant, mechanical modeling tools.

Mentor Name: Kathleen Coleman

Project Description: The candidate will design and analyze various thin metal compliant mechanisms that can be fabricated with a novel laser folding approach developed in ARL. The various designs will be fabricated in an iterative design process in a laser in ARL to perform controlled thermal folding. The ideal candidate will have experience and understanding of compliant mechanisms, mechanics of material, thermal effects and heat transfer. Innovation and creativity as well as hands on experimentation experience are desired. Moreover the candidate should possess excellent verbal and written communication skills. There is an expectation of a submitted journal article after the students period of performance ideally related to the candidates thesis work.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): Engineering

Preferred Technical Skills: Microsystems, 3D printing, MEMS, Additive Manufacturing, Nano-printing, Laser Forming

Mentor Name: Gabriel Smith

Project Description: Investigate and develop a module that would work with simulation allowing causal inferencing to explore what happens within the scenario is a systematic manner.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Military Information Sciences (MIS)

Required Major(s): Engineering, Computer Science, Mathematics

Preferred Technical Skills: Python, SQL, Flask

Mentor Name: Adrienne Raglin

Project Description: This research would focus on developing models that can identify causal relationships between variables when working with datasets that combine textual, visual, and other modalities where some data points are systematically missing. The challenge lies in distinguishing between missing data mechanisms (missing completely at random vs. missing not at random) and ensuring that causal discovery methods remain robust when entire modalities are absent for certain observations. The work would focus on developing identification strategies and estimation methods that can leverage information across modalities while accounting for the potential bias introduced by systematic missingness patterns.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Military Information Sciences (MIS)

Required Major(s): Engineering, Computer Science, Mathematics

Preferred Technical Skills: Python, SQL, Flask

Mentor Name: Adrienne Raglin

Project Description: This research would develop comprehensive benchmarks and evaluation frameworks to assess how well current LLMs understand causal relationships, including their ability to distinguish correlation from causation and reason about interventions. A key focus would be on identifying systematic biases in how these models approach causal questions and developing training methodologies to enhance their causal reasoning without compromising their general language capabilities.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Military Information Sciences (MIS)

Required Major(s): Engineering, Computer Science, Mathematics

Preferred Technical Skills: Python, SQL, Flask

Mentor Name: Adrienne Raglin

Project Description: Design and create a model to aid object detection and segmentation for image and video to aid in context based explainability.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Military Information Sciences (MIS)

Required Major(s): Engineering, Computer Science, Mathematics

Preferred Technical Skills: Python, SQL, Flask

Mentor Name: Adrienne Raglin

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).

Research Location: Adelphi, MD, On-site

ARL Research Competency: Photonics, Electronics, & Quantum Sciences (PEQS)

Required Major(s): Physics, Engineering, Computer Science, Applied Mathematics

Preferred Technical Skills: Optics and lasers, Electronics, Python programming, embedded system programming, physics modeling, computer aided design (CAD)

Mentor Name: David Meyer

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: 1. Piezoelectric sensor fabrication; 2. Advanced electronics packaging including chip to chip bonding; 3. Electrical characterization and testing; and 4. communicating and presenting research findings to ARL team. The successful candidate will primarily be stationed in Adelphi, Maryland.

Research Location: Adelphi, MD, Hybrid

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): Materials Science Engineering, Mechanical Engineering, Microelectronics, Electrical Engineering, Physics, or a related field

Preferred Technical Skills:

• Experience in material processing labs, cleanroom, and/or materials characterization lab
• Experience with microfabrication processes, specific to MEMS.
• Experience with IC packaging, chip to chip bonding and multichip packaging techniques
• Experience with IC design, and basic circuitry.
• Experience with MEMS applications and testing.
• Experience with COMSOL or relevant, mechanical modeling tools.

Mentor Name: Kathleen Coleman

Project Description: Distributed beamforming combines the signals from multiple radio transceivers to form beams in a desired direction.  The distributed transceivers act as a single phased array antenna.  This requires precise synchronization of the transceivers and precise knowledge of their locations. 

Distributed beamforming has commercial and military applications to increase the performance of small, low-powered transceivers with small antennas by combining them to create a single, large transceiver.  Application areas include radio-frequency sensing and communications.

Research Location: Adelphi, MD, Hybrid

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): Electrical engineering

Preferred Technical Skills: DEVCOM Army Research Laboratory is seeking a graduate student in electrical engineering for a summer internship working on technologies for distributed beamforming.  This work may focus on synchronization, positioning, or signal processing of the received signals.  Skills for the position include the ability to work with software-defined radios or radio-frequency systems on a chip (RFSOCs) and the ability to perform signal-processing analyses in MATLAB or Python.  The ideal candidate is someone working in this area as part of a thesis or dissertation. 

Mentor Name: Timothy Garner

Project Description: The Resilient Multi-Agent Systemsproject focuses on designing, developing, and testing advanced control and coordination strategies for multi-agent systems, including robotsand unmanned aerial vehicles (UAVs), to ensure reliable deployment and operation in contested and dynamic environments. Multi-agent systems are critical for modern battlefield operations, where autonomous platforms must collaborate to achieve mission objectives despite challenges such as communication disruptions, environmental uncertainties, and adversarial interference.
This project will explore cutting-edge techniques in distributed control, fault tolerance, and adaptive decision-making to enhance the resilience and effectiveness of multi-agent systems. The intern will work on developing algorithms, conducting simulations, and participating in field experiments to validate the performance of these systems in realistic scenarios.

Research Location: Adelphi, MD, Hybrid

ARL Research Competency: Military Information Sciences (MIS)

Required Major(s): Mechanical Engineering, Computer Engineering, Computer Science, Applied Math, Aerospace Engineering, 

Preferred Technical Skills: Python, ROS2, git, C++, docker

Mentor Name: Jeffrey Twigg

Project Description: The Resilient Multi-Agent Systemsproject focuses on designing, developing, and testing advanced control and coordination strategies for multi-agent systems, including robotsand unmanned aerial vehicles (UAVs), to ensure reliable deployment and operation in contested and dynamic environments. Multi-agent systems are critical for modern battlefield operations, where autonomous platforms must collaborate to achieve mission objectives despite challenges such as communication disruptions, environmental uncertainties, and adversarial interference.
This project will explore cutting-edge techniques in distributed control, fault tolerance, and adaptive decision-making to enhance the resilience and effectiveness of multi-agent systems. The intern will work on developing algorithms, conducting simulations, and participating in field experiments to validate the performance of these systems in realistic scenarios.

Research Location: Adelphi, MD, Hybrid

ARL Research Competency: Military Information Sciences (MIS)

Required Major(s): Engineering, Math

Preferred Technical Skills: ROS2, python, C++, docker, git

Mentor Name: Jeffrey Twigg

Project Description: We are developing a mm-wave radar for UAV imaging and detection, based on existing COTS from the automotive radar technology. We are planning to evaluate these components by performing controlled experiments for UAV detection.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): Electrical Engineering

Preferred Technical Skills: Experience with RF equipment, basic knowledge of radar technology and digital signal processing, Matlab programming

Mentor Name: Traian Dogaru

Project Description: Small drones have become very popular for providing enhanced situational awareness for human operators.  One issue is that drones need to be able to perceive and avoid obstacles as they traverse the environment.  Many methods of affecting obstacle avoidance are quite heavy (e.g. lidar).  We propose a novel method of perceiving the world that leverages the way insects see the world: Optical Flow.  Optical flow is the apparent motion of the visual field as an observer moves through the world.  By avoiding areas of "high" optical flow and steering towards areas of lower apparent motion, we can affect a very computationally efficient of visual perception.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Photonics, Electronics, & Quantum Sciences (PEQS)

Required Major(s): Aerospace, Electrical, Computer, Mechanical Engineering (or similar); or Computer Science

Preferred Technical Skills: ability to program in python (minimum); c++ desired.  Familiarity with ROS2 desirable.

Mentor Name: Joseph Conroy

Project Description: This objective of this research is to define a technical approach for a capability that enables passive ground-based sensors or receivers to identify individual sUAS flying 800-1000m away which may be in a clustered swarm of blue and red force UAS.  The technology should enable the ground-based sensor or receiver to detect/receive the 3D position of each blue force sUAS, have no-to little RF signature, and operate in both day and night flight conditions.  The technical solution should not require any new processing on board the uUAS but may source the sUAS for power and existing on board data. This topic may also investigate commercially available IFF solutions for drones that we may be able to exploit from “detect and avoid” into detect and engage”.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Photonics, Electronics, & Quantum Sciences (PEQS)

Required Major(s): Electrical Engineering, Physics (or equivalent)

Preferred Technical Skills: Optics, Materials, signal processing, remote sensing, Atmospheric sciences

Mentor Name: Steven Perry

Project Description: This objective of this research is to define materials and aerodynamically efficient form-factors for sUAS airframes which can sustain at minimum of five intentional in flight collisions with other sUAS platforms without sustaining damage to its own form-factor or propulsion system. 

Research Location: Adelphi, MD, On-site

ARL Research Competency: Photonics, Electronics, & Quantum Sciences (PEQS)

Required Major(s): Aerodynamics, Mechanical Engineering, Material Sciences (or equivalent)

Preferred Technical Skills: Aerobody Design, CAD Software

Mentor Name: Steven Perry

Project Description: In this project, we would like to design and build a small radar system for SAR imaging. The system will be based on the radar demonstration kit that we had from Pasternack. This radar will be used to test our small-scale metasurface prototypes.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): Electrical Engineering

Preferred Technical Skills: Electromagnetic, radar, signal processing

Mentor Name: Quang Nguyen

Project Description: Conduct experiments with existing ARL RF hardware to test distributed spectrum sensing concepts like signal detection, signal identification, and geolocation.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): Electrical Engineering, Computer Engineering, Software Engineering, Physics

Preferred Technical Skills: Matlab, python programming experience. Familiarity with RF equipment like software defined radios, vector network analyzers, spectrum analyzers, etc.

Mentor Name: Benjamin Kirk

Project Description: This role involves operating and automating our state-of-the-art photoluminescence and reflection mapping systems. The student will be responsible for developing scripts in either LabVIEW or Python to control instrumentation, acquire data, and analyze results. This opportunity requires the application of programming skills applied to cutting-edge research systems with real-world impact.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Photonics, Electronics, & Quantum Sciences (PEQS)

Required Major(s): Any Engineering Major; Computer Science; Physics, Chemistry

Preferred Technical Skills: Programming and scripting experience (specifically Python); background in National Instruments LabVIEW; familiarity with data file types and data acquisition; general lab and lab equipment experience/etiquette

Mentor Name: Matthew Chin

Project Description: The proposed Summer Student Experience effort will use the DECAPODES development environment to develop a simulation of a prototypical magnetohydrodynamic problem and conduct analyses comparing results with theoretical, experimental and modeling studies from the scientific literature.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Network Cyber & Computational Sciences (NCCS)

Required Major(s): Mathematics, computer science, physical sciences, engineering

Preferred Technical Skills: Some experience in exterior calculus and differential forms; Julia programming language; basic computational fluid dynamics techniques

Mentor Name: Benjamin MacCall

Project Description: High-performance broadband Power Amplifiers (PAs) are critical for Department of War applications in communications, radar, and electromagnetic warfare. This project is to support the design, fabrication, and test of broadband PAs in a variety of semiconductor process technologies.

Research Location: Adelphi, MD, On-sit

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): Electrical Engineering

Preferred Technical Skills: Cadence AWR/Microwave Office or Keysight ADS, RF test

Mentor Name: Matthew LaRue

Project Description: Reverse grading, heavy doping in the access regions, and specialized metallization stacks are proposed solutions to the ongoing challenge of establishing effective ohmic contacts on ultrawide-bandgap semiconductors. In this project, the student—preferably a senior or an early-stage graduate student—will:
(1) From a band diagram perspective, recognize eventualities for both ohmic and Schottky contacts based on material properties such as work function, electron affinity, and bandgap.
(2) Use COMSOL Multiphysics or another Poisson solver to simulate metal-semiconductor contacts and validate expectations from step 1.
(3) Measure the I-V characteristics on prefabricated metal-semiconductors structure and extract contact resistance.
(4) Prepare a technical report and presentation that clearly and quantitatively describe the characteristics of ideal metallization schemes for ohmic contact in UWBG semiconductors. If such schemes are not feasible, provide guidelines for ohmic contacts in ultrawide-bandgap semiconductors.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): Engineering (Electrical, Material Science, Mechanical), Mathematics, Physics

Preferred Technical Skills: TCAD modeling, Scientific reporting, Technical writing

Mentor Name: Franklin L Nouketcha

Project Description: Accurate modeling of semiconductors requires knowledge of material electrical and optical properties such as mobility, breakdown-field strength, ionization energy and transmission coefficients to name a few. Those properties can be obtained through highly theoretical work such as density functional theory calculations in conjunction with Monte Carlo simulations. They can also be obtained experimentally via various material characterization techniques. The goal of this project is to leverage tools at ARL to experimentally extract or validate key materials properties needed for the accurate TCAD modeling of ultrawide-bandgap semiconductors. In this project, the student will collaborate with key ARL scientists to:
(1) Perform material characterization on ARL grown UWBG semiconductors (AFM, XRD).
(2) Deposit metal for Hall and TLM measurements.
(3) Perform temperature dependent hall measurements for experimentally guided modeling of temperature-dependent mobility and dopant ionization.
(4) Draft a technical report and a presentation summarizing experimentally obtained material parameters (mobility, ionization energies) relevant for TCAD simulation.

Research Location: Adelphi, MD On-site

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): Engineering (Electrical, Electronics, Mechanical), Material Sciences, Physics, Mathematics

Preferred Technical Skills: Technical writing, Communication, Solid state physics

Mentor Name: Franklin L Nouketcha

Project Description: The broad aim of this project is to develop and improve the ability of autonomous systems to understand their surroundings and prioritize their processing of information at the edge in both real and simulated environments using visual perception and control models.  These models are aimed at supporting the larger decision making of autonomous or human agents and to help them operate within their surrounding environment.  Decision-making may require understanding the surrounding environment using a combination of low, mid, or high-level visual concepts and balancing the need to explore the unknown or exploit what is partially known to understand it better.
Potential projects may include:
• Developing test simulation environments and scenarios in Unity Unreal or other game engine systems.
• Setting up, simulating, or testing next generation computing models on edge devices or AI accelerators
• Constructing interfaces for mobile or specialized visual sensors to operate on the edge in real-time
• Collecting and constructing novel datasets for high dynamic range imaging or advanced Neuromorphic systems evaluation.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Military Information Sciences (MIS)

Required Major(s): Computer science, Electrical and Computer Engineering, Electrical Engineering, Mechatronics, Mechanical Engineering

Preferred Technical Skills: Python, C++/C#, Unity/Unreal/(or related game engine experience),
Computer Vision, Image Processing, Artificial Intelligence, Machine Learning, Control theory, computational photography
Experience with microcontrollers, PCB development, electrical hardware integration, robotics, fpga programming

Mentor Name: Andre Harrison

Project Description: Impact ionization coefficients (IICs) are essential for modeling the breakdown characteristics of power devices and simulating the avalanche gain for photonics devices. The extraction of IICs is possible for Si and the wide-bandgap semiconductors but remains a challenge for the ultrawide-bandgap semiconductors, due to reliability challenges associated with high-voltage operations. In this project, the student will interact with various research teams at ARL to:
(1) Review and apply existing methodologies for experimentally (through I-V measurements) and theoretically (through Monte Carlo simulations) extracting IICs for Semiconductors.
(2) Review alpha- and beta-voltaic technologies with an emphasis on the quantification of electron hole pairs generations as a function of radiation energy
(3) Correlate injected particle energy into an effective internal electric field using more mature material like 4H-SiC for which IICs are more well understood.
(4) Based on calibrations in step 3, extract impact ionization coefficients in UWBG semiconductors by means of EBIC, alpha- and beta-voltaic.
(5) Draft a technical report and draft a presentation of key findings.

Research Location: Adelphi, MD, On-site

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): Engineering (Electrical, Electronics, Mechanical), Material Science, Physics, Mathematics

Preferred Technical Skills: Energetic Materials, Reactive Materials, Materials Characterization, Spectroscopy, Optical Diagnostics, Metal Powders, Thermal Characterization, Lasers

Mentor Name: Franklin L. Nouketcha

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 to be implemented on a smartwatch-class microprocessor.  The intern will implement 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, then integrate the ‘best’ AIML algorithm for demonstration on current sensor system(s).

Research Location: Adelphi, MD, On-site

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): Computer Science, Computer Engineering, Electrical Engineering

Preferred Technical Skills: coding

Mentor Name: Sean Heintzelman

Project Description: There is an abundance of historical data on the electrical activity of power systems across the USA, renewable energy sources, and electric cars.   Many sensors are even forwarding new data in real time to research current trends! Modern energy systems not only need to accurately report current power-usage data, but have the ability to predict future electrical activities to ensure power is readily available for soldiers and civilians at all times.  Interns for this project will write software to analyze electrical power data for magnitude, phase angle, harmonic distortion, etc. to recognize patterns in power data.  Once patterns of interest have been recognized, the student will then write real-time statistical or machine-learning algorithms to spot those patterns before they actually occur. 

Research Location: Adelphi, MD, On-site

ARL Research Competency: Electromagnetic Spectrum Sciences (EMSS)

Required Major(s): applied mathematics, computer science, electrical engineering, computer engineering

Preferred Technical Skills: Coding, applied mathematics

Mentor Name: Sean Heintzelman

Project Description: The aim of this research is to develop theoretical and experimentational approaches for generalized AI model inference acceleration on resource constrained heterogeneous edge computing platforms. This research is also aimed at predicting optimal AI model architecture through neural network architecture search (NAS) to achieve expected inference acceleration. The research covers both convolutional neural networks and large language models for their inference acceleration.
This project covers the following research topics:

Develop mathematical models to understand the trade-off between accuracy, latency, and compression of optimized AI models.
Investigate state-of-the-art, quantization, model pruning and other AI model computational complexity reduction approaches for inference acceleration on edge computing platforms with resource constraints.
Formulate Mathematical theoretical foundations and mathematical models to guide the optimization process and ensure convergence to optimal solutions while satisfying all the constraints.
Develop layer-wise gradual optimization approaches.

Research Location: Aberdeen Proving Ground, MD

ARL Research Competency: Network Cyber & Computational Sciences (NCCS)

Required Major(s): Faculty and post-graduate fellows with related experience are preferred

Mentor Name: Dasari, Venkateswara

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 optimum pinhole array configurations (spacing, hole size, etc.) of the system for expected fireball scenes. The system will be calibrated using the optimum pinhole array and a blackbody furnace. Students will collect internal fireball data from metallized and conventional HE charges with simple detonation experiments. Time permitting, data processing/management routines will be established.

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

ARL Research Competency: Weapons Sciences (WS)

Required Major(s): Mechanical Engineering, Chemical Engineering, Materials Science & Engineering, Applied Physics, Physics, Electrical & Computer Engineering

Preferred Technical Skills: high-speed videography, spectroscopy, pyrometry, energetic materials, reactive materials, optics, photonics

Mentor Name: Elliot Wainwright

Project Description: Laser-driven flyer plates (LDFP) can be utilized as a small-scale platform to induce high-pressure and shock conditions to powder beds. LDFP uses a focused, high-power laser to propel a small impactor into a sample of interest. This project is focused on using LDFP to impact a wide range of metal powders of varying composition to understand the mechanical and chemical response of the material under shock loading. Project will include learning the LDFP system, collecting data for a variety of samples, processing the data, and compiling the results into a final report.

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

ARL Research Competency: Weapons Sciences (WS)

Required Major(s): Materials Science & Engineering, Mechanical Engineering, Chemical Engineering, Physics, Applied Physics, Chemistry

Preferred Technical Skills: Optics, Lasers

Mentor Name: Elliot Wainwright

Project Description: The intern is expected to perform characterization for untreated and surface-treated aluminum nanoparticles using experimental and/or simulation modeling approaches. Experiments will aim to study properties including but not limited to porosity, particle size distribution, surface chemistry, morphology, and chemical composition using different techniques and equipment available in the group and at DEVCOM ARL. Simulation models will be attempted using the COMSOL Multiphysics available at DEVCOM ARL and in collaboration with industrial partners.

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

ARL Research Competency: Weapons Sciences (WS)

Required Major(s): Engineering and Sciences majors, such as Materials Science and Engineering, Chemical Engineering, Mechanical Engineering, Chemistry, Physics

Preferred Technical Skills: Experience and knowledge in materials characterization such as SEM, TEM, XRD, XPS, DLS, particle analysis, gas sorption, analytical chemistry such as TGA/DSC, FTIR, elemental analysis. Experience in plasma synthesis or surface processing is a plus but not required. Hands-on experience in plasma simulations using COMSOL will be a huge plus but not required.

Mentor Name: Chi-Chin Wu

Project Description: We are seeking 3D Printing and 3D Design specialists to join our team. The ideal candidate will be responsible for creating, optimizing, and producing high-quality 3D models and prototypes using advanced 3D design software and 3D printing technologies. This role requires a strong understanding of design principles, materials, and manufacturing processes, as well as the ability to collaborate with cross-functional teams.

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

ARL Research Competency: Science of Extreme Materials (SEM)

Required Major(s): STEM

Preferred Technical Skills: CAD and CAM; Python or higher level programming skills; Robotic programming

Mentor Name: Jian Yu

Project Description: Researcher will perform a project in support of the ARL Cold Spray Additive Manufacturing Team. Topics may include: development of new materials for Cold Spray processing, characterization of Cold Sprayed materials, or robotic programming for Cold Spray.

Research Location: Aberdeen Proving Ground, MD On-site

ARL Research Competency: Science of Extreme Materials (SEM)

Required Major(s): Mechanical Engineering, Chemical Engineering, Physics, or other similar fields

Preferred Technical Skills: Oral communication, programming, mechanics, materials characterization

Mentor Name: Isaac Nault

Project Description: This project involves investigation to the structural changes of novel metal alloy materials. The project goals are as follows: high-pressure x-ray diffraction collection and structure refinement using diamond anvil cells. Correlation of several data sets of different alloys blends to understand changes in properties with composition. Alloys to be studied will be prepared in-house with focus on iron based alloys. High pressure work involves micrograms of material and uses modern high-pressure techniques and synchrotron x-ray radiation.

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

ARL Research Competency: Weapons Sciences (WS)

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

Preferred Technical Skills: Experience with x-ray diffraction techniques. General material property knowledge. High pressure diamond anvil cell.

Mentor Name: Timothy Jenkins

Project Description: This project involves the development and testing of composite materials made of at least two different types of components. These materials are used in a wide range of applications and may include fiber reinforced composites, metal-matrix composites, and polymer matrix composites. Participants will gain experience in one or more of the following: mechanical property testing, thermal property testing, chemical synthesis, composite processing, X-ray spectroscopy, and electron microscopy.

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

ARL Research Competency: Science of Extreme Materials (SEM)

Required Major(s): Chemical engineering, chemistry, mechanical engineering, materials science, physics, civil engineering.

Preferred Technical Skills: Laboratory experience (university coursework is sufficient).

Mentor Name: Daniel Knorr

Project Description: Opportunities exist for foundational and early applied 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, 2) advanced manufacturing science for development of heterogeneous multi-scale ceramics and interfaces with high thermomechanical performance, 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.

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

ARL Research Competency: Science of Extreme Materials (SEM)

Required Major(s): Materials Engineering, Mechanical Engineering, Chemical Engineering, Chemistry

Preferred Technical Skills: 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.

Mentor Name: Lionel Vargas-Gonzalez

Project Description: This project will involve the synthesis, characterization of reactive and energetic materials with applications in explosives, propellants, and pyrotechnics, and/or development of novel, high-speed optical diagnostics system for characterizing extreme temperature and rate reaction events.

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

ARL Research Competency: Weapons Sciences (WS)

Required Major(s): Materials Science & Engineering, Mechanical Engineering, Chemical Engineering, Applied Physics, Chemistry

Preferred Technical Skills: Energetic Materials, Reactive Materials, Materials Characterization, Spectroscopy, Optical Diagnostics, Metal Powders, Thermal Characterization, Lasers

Mentor Name: Elliot Wainwright

Project Description: Machine Learning (ML) and data science has become an integral part of many domains (e.g., image analysis, networking protocols, network security, etc.), resulting in increased motivation on applications to cyber defense tools. Furthermore, the rapid rate of attacks and immense volume of data, significantly increases the demand on a small number of human analysts. Necessitating, the use of data science and ML techniques to enable the scalability and reduced demand on human analysts. However, there are many challenges in the successful use of data science and ML for cyber security problems. Increasingly, supervised learning relies on a significant amount of quality labeled data. To avoid the requirements of a significant amount of labeled data, it is necessary to innovate semi-supervised methodologies in a resource constrained domain for network communications in the cyber domain. In the network/communications domain, machine learning based classifiers are generally trained within a closed environment. Specifically, datasets used for training and evaluation are static and do not vary. Conversely, network environments are dynamic over time. Adversaries’ attacks become more sophisticated and change in response to defenders’ actions, requiring a defender to retrain a classifier to reflect the new attacks in the intended environment for deployment.

This research is focused on data science and ML applications to network traffic (i.e., network traffic analysis, network forensics). Example key research questions include the following:
• How do we design ML based network traffic classifiers using a limited amount of data?
• How do we leverage ML for network traffic classifiers in a resource constrained environment?
• How can we apply ML to network forensics or traffic analysis problems? 

Research Location: Aberdeen Proving Ground, MD, Hybrid

ARL Research Competency: Network Cyber & Computational Sciences (NCCS)

Required Major(s): Computer Science, Computer Engineering, Cyber

Preferred Technical Skills: Programming (i.e., Python), Networking Background, Machine Learning, Cyber Security

Mentor Name: Michael De Lucia

Project Description: This group project aims to enhance human cognition by leveraging foundation models to support memory, mental model formation, communication, and decision making in real-time amongst Soldiers. Students of diverse backgrounds will collaborate in a multi-disciplinary (e.g. computer science, artificial intelligence, neuroscience, cognitive science, and human-computer interaction) effort to research human behaviors, human-machine interactions, and AI system design. Students will create novel methods to research and develop human-AI systems interactions that can lead to augmented development and maintenance of situational understanding during complex and dynamic human interactions.

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

ARL Research Competency: Humans in Complex Systems (HCxS)

Required Major(s): Computer Science, Cognitive Science, Human-Computer Interaction, Electrical Engineering

Preferred Technical Skills: Programming: Retrieval augmented generation (RAG), model finetuning/ML techniques, data representations, user-interface development, prompt engineering, data generation/analysis

Experimental Design/Academic Research: Experimental protocol development, dataset generation, data collection/analysis (e.g., designing pipelines, statistical significance testing). Human subjects research is a plus.

Human-Centered Design: Pilot studies, user-informed iterative development, rapid prototyping (e.g., hardware and software interfaces)

High quality communication skills (e.g., academic writing, presentation) are a plus. There will be plenty of opportunities to develop these throughout the program.

Mentor Name: Mariela Perez-Cabarcas

Project Description: Toward enabling decision dominance in the ever-increasing data-centric tactical environment, it is critical to assign and adapt data processing tasks such that we can support the greatest quality, quantity, and speed of data analysis in resource-constrained dynamic environments. Participants will work side by side with DEVCOM ARL researchers on an ongoing research project to enable efficient and effective data processing in constrained environments. The active areas of this project include signal processing and AI, AI application adaptation for dynamic execution, application characterization, data visualization, distributed computing and system integration.

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

ARL Research Competency: Network Cyber & Computational Sciences (NCCS)

Required Major(s): One or more of: Computer Science, Computer Engineering, Electrical Engineering

Preferred Technical Skills: Any subset of Software Development, Data Engineering, Data Visualization, Machine Learning, Signal Processing, Distributed AI, Systems Engineering, Front-End Development

Mentor Name: Matthew Dwyer

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). 

Research Location: Austin, TX, On-site

ARL Research Competency: Photonics, Electronics, & Quantum Sciences (PEQS)

Required Major(s): Physics, CompSci, Engineering, Applied Mathematics, or similar

Preferred Technical Skills: Optics and lasers, Electronics, Python programming, embedded system programming, physics modeling, computer aided design (CAD)

Mentor Name: Paul Kunz

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.

Research Location: TBD, On-site

ARL Research Competency: Terminal Effects (TE)

Required Major(s): Mechanical Engineering, Biomedical Engineering, Physics, Mathematics, Computer Science

Preferred Technical Skills: Office Products, Computer Programming

Mentor Name: Karin Rafaels