The DEVCOM Army Research Laboratory (ARL) Research Associateship Program (RAP) Summer Student Experience (SSE) is an educational program that allows undergraduate through PhD students and recent bachelor’s and master's degree graduates to participate in a paid research experience at a Department of Defense laboratory over the summer break. Participants are paired with scientists and engineers at ARL who are helping to shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs.

As a participant in the ARL-RAP SSE, you will be part of high priority research efforts that are broadly supported by 12 research competencies. DEVCOM ARL has identified research topics available within several of the competency areas. Opportunities for general research under each competency are also available. 


Biological related disciplines, including synthetic biology, biological materials, biological/abiological interfaces, and biological effect.


Science of mechanical and electrical power generation, storage, conditioning and distribution; energy conversion; and emerging concepts for lasers, directed energy (DE), DE protection and propagation.

ES-1:  Modeling and Simulations of Battery Materials

ALC* - Adelphi Laboratory Center, Maryland

The selected candidate will work in the area of molecular scale modeling of battery electrolytes, implement and apply numerous molecular to mesoscale level computational methodologies spanning from density functional theory (DFT), molecular dynamics simulations of bulk battery electrolytes and electrochemical interfaces and complex/composite interphases. Some of the following methods will be applied.

  • Density Functional Theory (DFT) calculations: density functional theory calculations from the clusters to the liquid – solid interfaces to electrochemical processes, training of the ML-based model based on DFT/QC calculations.
  • Molecular Dynamics Simulations: MD simulations from bulk electrolytes to electrolytes at electrified interfaces with the focus on electrochemical reactions within electrochemical double layer. Developing of novel force fields to enable high fidelity prediction of charge transport and charge – transfer processes. Knowledge of programming languages such as C++, Fortran, Python, community simulation codes and computational frameworks in highly desired.

ES-2:  Nuclear Physics Research at The US Army Combat Capabilities Development Command Army Research Laboratory

ALC - Adelphi Laboratory Center, Maryland

DEVCOM Army Research Laboratory (ARL) research into nuclear physics is focused on the measurement of fundamental physical quantities relevant to nuclear properties and reaction cross sections.  The project will encompass active participation in nuclear measurements, development of detection systems, or analysis of nuclear data.

ES-3:  Oxysulfide Li-ion Glassy Ion Conductor

ALC - Adelphi Laboratory Center, Maryland

The student will evaluate the processing of oxysulfide conductors in dryroom conditions for practical battery manufacturing.  The use of glassy ionic conductors has been proposed for development of an all-solid-state battery for both mobile and stationary applications. One barrier to commercialization is the processing of these materials into thin films as they are reactive towards water.  Recent work has suggested that certain glassy compositions can be prepared that are stable in dryroom environments and that processing into lithium electrochemical cells is viable in commercially relevant conditions. This project will evaluate the processing and stability behavior of these glassy ionic conductors under a range of dryroom conditions.


Novel approaches to sensing and operating across the entire electromagnetic (EM) environment; counter-sensing across the EM spectrum; protection from EM effects; emerging concepts for RF, radars, and electronic warfare (EW).

ESS-1:  Logic and EM Designs for Metasurfaces/RIS

ALC* - Adelphi Laboratory Center, Maryland

Developments of the subject of metasurfaces and RIS (Reconfigurable Intelligent Surfaces) have been the subject of intensive studies in recent years. Many studies have been focused on reflection mechanisms generated from low-profile apertures.  The generation of the directed beams at different frequencies over wide bands, with pre-defined shapes, and according to certain timing patterns, was the subject of EM simulations using known computer-generated tools.  To implement such tools at the system level, control mechanisms, time and frequency multiplexed operations, as well as phase shifting of feeding signals, become essential logic operations.  The understanding and execution of simultaneous EM and logic operations show the need of both talents within the same design team.  It also creates the need for innovative software and logic tools to produce the efficient overall system.

ESS-2:  Transparent Antenna

ALC* - Adelphi Laboratory Center, Maryland

In the next generation of wireless communication system, electronic devices are becoming smaller and thinner, which result in less space for antenna. However, miniaturized antenna will suffer from low radiation efficiencies due to smaller footprint. In this abstract, we are looking at the concept of transparent antenna. It means that antenna is invisible to human eye. Thus, by incorporating the invisible antenna to the frame of electronic devices, we don’t necessarily have to sacrifice the real estate for smaller antenna with much less ideal performance. We can utilize the whole area of device’s frame for antenna if needed since the antenna is transparent. To validate the feasibility of this concept, we will design and simulate the wire grid antenna. The optical transparent rate can be adjusted by tuning the grid patterns such as, grid widths and spacing. The efficiency of antenna will be studies with different conductive paste, such as Cu, or Ag paste.


Multi-disciplinary non-medical approaches to understand and modify the potential of humans situated in and interacting within complex social, technological, and socio-technical systems.

HCxS-1:  Bi-Directional Adaptation

APG* - Aberdeen Proving Ground, Maryland

This project aims to revolutionize communication between Soldiers and systems, going beyond traditional methods. By focusing on real-time multimodal interactions, it will explore innovative solutions for team-level trust calibration, cohesive team dynamics, dynamic information presentation, and optimizing human-system performance in real-time. This will involve researching and developing technologies that enable Soldiers to communicate with systems as naturally and efficiently as they do with fellow humans, utilizing speech, gestures, and other forms of body language. 

HCxS-2:  Estimating and Predicting Human Behavior

APG* - Aberdeen Proving Ground, Maryland

Focusing on the variability of human behavior within complex systems, this project will develop techniques to sense, interpret, and predict changes in human states such as stress, fatigue, and intent. By understanding these human elements, the project aims to adapt technologies more effectively and infer operational environment contexts, thus enabling intelligent systems to better comprehend and collaborate with their human counterparts.

HCxS-3:  Human-Guided System Adaptation

ALC* - Adelphi Laboratory Center, Maryland

This project addresses the rapid evolution of military and civilian AI technologies. It will develop methodologies allowing Soldiers to guide the adaptation of these technologies effectively. This includes creating interfaces and protocols for Soldiers to interact with and steer the development of intelligent systems, ensuring that these technologies remain relevant, useful, and upgradable in rapidly changing combat environments

HCxS-4:  Human-System Teaming

APG* - Aberdeen Proving Ground, Maryland

This project seeks to understand and leverage dynamic interactions within human-system teams. It will develop principles for effective collaboration between Soldiers and intelligent systems, focusing on emergent team properties, variability in performance, shared situational understanding, and dynamic task allocation. Special emphasis will be on adapting to changing conditions, such as loss of capabilities, shifting goals, and adversarial interference.

HCxS-5:  Hybrid Human-Technology Intelligence

APG* - Aberdeen Proving Ground, Maryland

The focus here is on anti-disciplinary research to enhance human-system teams in multi-domain operations. This involves pioneering hybrid approaches that integrate human cognitive capabilities with advanced technology. The project will study the bottlenecks in human cognition, develop technological solutions to overcome these, and explore new methods to leverage human neural processing for creating or enhancing intelligence within human-system teams.

HCxS-6:  Neuroscience and Neurotechnologies

APG - Aberdeen Proving Ground, Maryland

This project aims to harness the human nervous system's capabilities to enhance Soldier performance and drive the development of intelligent systems. It will integrate neuroscience with Soldier System teams to create enhanced mixed systems and use insights from the nervous system to develop novel computational approaches. This dual approach seeks to leverage the efficiency and adaptability of the human nervous system for advanced technological applications.

HCxS-7:  Next-Gen Materials and Tools for Neuroscience Research

APG - Aberdeen Proving Ground, Maryland

This project entails design and investigation of the electrical, biomagnetic, and functional properties of various materials, models, and tools used for neuroscientific study of brain, skull, and/or scalp tissue. Work bridges areas of neuroscience, materials science, biochemistry, and biomedical engineering and involves a variety on hands-on laboratory testing and analysis procedures.


Underpinning sciences, physical autonomy, and enablers required to provide timely, mission-aware information to humans and systems at speed and scale for all-domain and coalition operations. 

MIS-1:  Artificial Reasoning, Information Science, Human Information Interaction Research

ALC* - Adelphi Laboratory Center, Maryland

A fellowship opportunity is available with the DEVCOM Army Research Laboratory's (ARL) located in Adelphi, MD. ARL is seeking a postdoc with a background in computational modeling, artificial intelligence, machine learning, and analysis.

Research opportunities are available to develop methods to create algorithms and methodologies that enable efficient computational models for recommendations and informed decisions by capturing individual characteristics of users, tasks, and context including domain knowledge and situational awareness, agents’ behavior, and decision outcomes. Research will ultimately allow the generation and the deployment of intelligent information systems that incorporates multiple levels and approaches for reasoning. Research requires extensive experience in interactive visual systems, artificial intelligence, machine learning, and analysis of data from various modalities.

MIS-2:  Combining Data-Driven Learning with Prior Knowledge for Improved Target Classification over Non-imaging Sensors

ALC* - Adelphi Laboratory Center, Maryland

Advances in AI/ML over the last 10 years have led to superhuman performance of classifiers operating on image and video streams.  More recent advances in unsupervised training for foundation models such as large language models have led to impressive zero-shot learning results.  Neuro-symbolic approaches provide mechanisms to combine prior knowledge as rules with data-driven learning enabling effective performance using small training datasets. While much research in these areas have focused on computer vision, less attention has been devoted to less power hungry sensing assets such as acoustic and seismic sensors.  This project intends to incorporate prior knowledge such as physical constraints into data-driven learning possibly leveraging foundation models to develop better performing acoustic and/or seismic target detectors/classifiers.

MIS-3:  Cross-Reality (XR) Research and Development

ARL-WEST* - Playa Vista, California

In this project you will support the design and development of AR/VR/XR applications that align with the objectives of ARL’s Military Information Sciences.

Responsibilities include:

  • Design and implement applications using Unity for XR devices, with a strong emphasis on optimizing performance and maintaining high-quality standards.
  • Integrate the latest XR technologies and research findings into the research and development process to create novel and impactful applications.
  • Conduct experiments and user studies to gather feedback and insights, iterating on designs to improve user experience and overall project success.
  • Collaborate closely with multi-disciplinary teams, including researchers, designers, and data scientists, to analyze data, draw meaningful conclusions, and present findings.

You will be selected based on the following qualifications:

  • Excellent problem-solving skills and the ability to think creatively to overcome technical challenges.
  • Outstanding communication and collaboration skills to work effectively in a research-driven team environment.
  • Experience in developing AR/VR/XR and applications for various XR devices.
  • Experience in conducting user studies, usability testing, and data analysis to inform design decisions and research outcomes.

MIS-4:  Data Scientist/Machine Learning for Recommender

ALC* - Adelphi Laboratory Center, Maryland

This opportunity is focused on information management research and development using the latest machine learning techniques.  As a Data Scientist/Machine Learning Researcher, your primary responsibility will be to support the design and development of ML applications that align with the objectives of ARL’s Military Information Sciences. You will work with multi-disciplinary teams across the country to develop the ML algorithm to advance Army’s future capabilities.

Responsibilities include:

  • Develop machine learning model with optimized performance and high-level accuracy in prediction.
  • Integrate the latest machine learning methodology into the research and development process to create novel and practical applications.
  • Conduct experiments to iterate and improve the model.
  • Collaborate closely with multi-disciplinary teams, including researchers, designers, and data scientists, to analyze data, draw meaningful conclusions, and present findings.       

You will be selected based on the following qualifications:

  • Excellent problem-solving skills and the ability to think creatively to overcome technical challenges.
  • Outstanding communication and collaboration skills to work effectively in a research-driven team environment.
  • Experience in recommender system, reinforcement learning, and/or Graph Neural Network (GNN).

MIS-5:  Human Language Technologies, Large Language Models (LLM) and Multilingual Computing

ALC - Adelphi Laboratory Center, Maryland

This opportunity is focused on Human Language Technologies, Large Language Models (LLM) and multilingual computing.  As a Multilingual Computer Scientist, your primary responsibility will be to utilize LLMs to expand our military's ability to overcome barriers to spoken communications with local nationals, none-English speaking security partners and victims of natural disasters.  You will work with multi-disciplinary teams engaged in the Americas, in Europe and in the Indo-Pacific region seeking to provide Soldiers with technology to help them understand and be understood by foreign language speakers, even in austere environments, including operations at the tactical edge.

Responsibilities include:

  • Develop LLM to enable a new generation of speech translation systems, optimizing performance and maintaining high-quality standards on small hardware platforms.
  • Conduct experimentation on the use of neuromorphic processors to execute Natural Language Processing (NLP) tasks.
  • Collaborate closely with multi-disciplinary teams, including researchers, software engineers, and computational linguists, to analyze data, draw meaningful conclusions, and present findings.

You will be selected based on the following qualifications:

  • Excellent problem-solving skills and the ability to think creatively to overcome technical challenges.
  • Outstanding communication and collaboration skills to work effectively in a research-driven team environment.
  • Experience in NLP and pre-trained LLM using Python, C, C++ or other relevant program languages.

MIS-6:  Machine Learning and Computer Vision/Graphics

ARL-WEST* - Playa Vista, California

This research aims to apply and develop AI/ML to visual perception problems. Advanced machine learning techniques, particularly generative neural models and deep learning will be explored. The work includes pursuing technical solutions and developing core algorithms. Anticipated research results include new theory and algorithm developments leading to publications in scientific forums and real-world utility and software for demonstrations.

MIS-7:  Visual Perception for Decision Making

ALC - Adelphi Laboratory Center, Maryland

This internship opportunity is focused on developing and implementing visual perception models to extract relevant visual information from an image.  As a computer vision Researcher, you will develop or implement these models to support the decision making of autonomous or human agents to help them understand their surrounding environment.  Decision-making may require understanding the surrounding environment using a combination of low, mid, or high-level visual concepts.


  • Develop or Implement computer vision models.
  • Conduct experiments.
  • Collaborate closely with multidisciplinary teams, including researchers, designers, and data scientists, to analyze data, draw meaningful conclusions, and present findings.
  • Write technical documentation and contribute to research papers.


  • Excellent problem-solving skills and the ability to think creatively to overcome technical challenges.
  • Outstanding communication and collaboration skills to work effectively in a research-driven team environment.
  • Experience in one or more related areas of computer vision such as object recognition, instance segmentation, panoptic segmentation, scene graph generation, amodal perception, pose estimation, open-set recognition, zero-shot learning, high dynamic range processing, and visual saliency modeling are preferred, but not required.


Science of novel mechanics, mechanisms, and control to enable manned/unmanned ground and air vehicle concepts.

MS-1:  Small UAS Platform Design and Controls Research

ALC* - Adelphi Laboratory Center, Maryland

ARL seeks research in platform design and control that will enable maneuverable, adaptive tactical mobility platforms for ground and air vehicles. Technical challenges include developing a computational framework for automated design of mechanical systems (soft/compliant robots and platforms) capable of performing morphological computation. Furthermore, control paradigms need to be developed for non-linear adaptive structures for which maneuverability is being maximized. The goal is to address (1) structural adaptations (2) describing structural and aerodynamic performance in dynamic environments as well as and perhaps most especially (3) controlling these highly coupled vehicles.


Sciences to enable and ensure secure resilient communication networks for distributed analytics in Multi-Domain Operations.

NCCS-1:  Adaptive Machine Learning Analytics in Resource-Constrained, Dynamic Environments

APG - Aberdeen Proving Ground, Maryland

Distributed ML analytic applications leverage multiple devices to optimize analytic performance at the expense of greater network and compute resource usage. However, compared to single device analytics, distributed analytic applications are more susceptible to dynamics in resource-constrained, dynamic environments because of the increased number of devices and network links involved. This project will explore methods to mitigate the impacts of degraded resources in ML analytic applications. Specifically, the participant(s) will work with DEVCOM ARL scientists to investigate, develop, and analyze adaptive ML analytic algorithms in resource-constrained, dynamic environments The participant(s) will have exposure and hands on experience with software development using Python, ML packages (PyTorch,Tensorflow) and data science packages (numpy, pandas), as well as analysis using visualization tools including Grafana, seaborn, and matplotlib. 

NCCS-2:  Heterogeneous and Low Probability of Detection Wireless Networks

ALC* - Adelphi Laboratory Center, Maryland

Multiple summer interns (both at undergraduate and graduate level) are sought to support projects focusing on intelligent heterogeneous networks which have been shown to have potential for enhancing the resilience and security of wireless communications networks by intelligently and adaptively exploiting multiple communications technologies operating at different parts of the electromagnetic spectrum (i.e., low frequency RF to Optical). The interns will work closely with ARL researchers on a variety of research tasks including theory, analysis and modeling, as well as experimental research. 

NCCS-3:  Machine Learning for Security and Security for Machine Learning

APG* - Aberdeen Proving Ground, Maryland

Machine Learning has become an integral part of many domains (e.g., image analysis, networking protocols, network security, etc.), resulting in increased integration of ML into cyber defense tools. One way in which adversaries have responded is by perturbing inputs to cause misclassification to achieve their objective. This type of attack is known as adversarial machine learning (AML). Cybersecurity-related defenses to AML should strive to defend against unseen attacks and not require constant updating based on newly discovered attacks. Increasingly, supervised learning relies on a significant amount of labeled data to perform supervised learning. To avoid the requirements of a significant amount of labeled data, it is necessary to innovate self-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 seeks to address key research questions, such as:

  • How do we design ML for cyber classifiers using a limited amount of data in a resource constrained environment?
  • How do we innovate network communication classifiers that are adversarial resilient?

NCCS-4:  Resource-Constrained Adaptive Computing Algorithms & Optimizations for AI Phoenix Stack

APG - Aberdeen Proving Ground, Maryland

Most Army systems have limited computation, communication, and power capabilities due to requirements for low Size, Weight, and Power (SWaP). Tactical devices may not have sufficient computational capability to process the live video, audio, or images from sensors to meet real-time mission requirements. As the Army is embracing AI to accomplish its mission goals, resource constraints of tactical computing platforms will impede the deployment of complex AI algorithms over tactical platforms. AI model optimization algorithms will be researched to reduce the model complexity and accelerate inference to achieve real-time performance in resource-limited edge platform. 

Tactical unmanned ground vehicles (UGVs) with limited computing resources are reliant upon resource constraint-aware adaptive computing algorithms for real-time operations. Autonomous AI stacks in UGVs utilize complex deep neural network (DNN) algorithms for intelligent navigation, computer vision and for exhibiting tactical behaviors in contested and constrained environments. Only after computational complexity reduction and model optimization of DNN algorithms are these algorithms executable for real-time on UGVs with limited resources. The aim of the project is to develop MDO-capable optimized, adaptive algorithms for reducing computational complexity of various learning, perception algorithms associated with the Autonomy AI stack supporting autonomy on UGVs. There are three technique approaches to optimize the DNN models in our efforts. First is to optimize the perception algorithm by reducing the computational complexity. Second is to optimize the neural networks to accelerate inference by exploiting the hardware properties. Third is to develop adaptive algorithms that could adapt to the device resource constraints to allow algorithms to meet their performance objectives. All these approaches are compliment to each other and can be combined to achieve better performance.

NCCS-5:  Tactical Network Security Using Deception

ALC* - Adelphi Laboratory Center, Maryland

This research opportunity is to develop novel methods for cyber deception. The candidate will be responsible for leading the design, development, publication, and prototyping of novel adaptive, proactive, and reactive cyber deception systems to ensure authentic, accurate, secure, and reliable communication networks. The developed models should focus on the generation, deployment, design, and reconfiguration of decoy devices such as honeypots, honeynets, honey-tokens, etc. Algorithms should apply to complex adversarial decision-making based on game theory, reinforcement learning, and utilizing state-of-the-art AI algorithms to dynamically adapt to, and learn from human or agent actions and contextual situations.


Materials (and related manufacturing methods) and devices intended for achieving photonic, electronic, and quantum-based effects.

PE&QS-1:  Long-Baseline Entanglement Distribution for 171Yb+ qubits

ALC - Adelphi Laboratory Center, Maryland

The 171Yb+ ion qubit (T2 >> 1 s) underpins leading academic and commercial efforts to build general-purpose NISQ-scale computing nodes. Extension of this approach to impactful quantum computing requires a mechanism to network compute nodes: photon-mediated ion-ion entanglement is the leading approach. An effort in ARL’s Quantum Science Branch is exploring entanglement distribution over long baseline optical fiber links. Summer 2024 projects will be devised based on project needs and guest researcher background. Topics in 2022-2023 included the following:

  • Design of a molecular dynamics model of nonlinear phenomena in laser-cooled ions in a ring-trap with an application to quantum random access memories.
  • Laser offset locking using the Pound-Drever-Hall (PDH) technique enhanced by optical serrodyne modulation.
  • Application of a 760 nm distributed Bragg reflector laser (DBR) to improve the repump rate of the 2F7/2 state in Yb+ qubits.
  • Design of a long working distance, high numerical aperture objective lens for efficient coupling of single 1650 nm photons to SMF-28 optical fiber.
  • Construction of a fiber-based self-heterodyne apparatus for characterization of laser frequency noise in the near-infrared.

PE&QS-2:  Meta-optics for Photonic Integrated Circuits

ALC - Adelphi Laboratory Center, Maryland

The U.S. Army Combat Capabilities Development Command Army Research Laboratory serves as the fundamental research facility for the U.S. Army. During this summer project, student researchers will gain the chance to engage in numerical modeling and experimental characterization of nanometer-scale structures for applications in photonic integrated circuits.


Materials and related manufacturing methods focusing on mechanical response and performance extremes, including active, adaptive, and flexible/soft materials; novel manufacturing science for energetic materials. 

SEM-1:  3D Printing of Electronic Materials for Electronics

ALC - Adelphi Laboratory Center, Maryland

Investigate the processing of materials suitable for 3D printed electronics and characterize the mechanical and electrical properties. These materials include but not limited to, conductive and non-conductive materials, with and without particles for substrate, interconnects, and adhesives for electronics. These processes will be compatible with 3D printing, thermal cycling, photonic sintering, and other preparation and curing methods. 

SEM-2:  Ceramics Processing and Manufacturing Sciences for Extreme Materials

ALC - Adelphi Laboratory Center, Maryland

Opportunities exist for foundational and early applied research and development (R&D) efforts towards enabling the next generation ceramics and glasses 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 fracture and failure tolerance, 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. 

Candidates of interest are U.S. Citizens in a degree program for Ceramic Engineering, Materials Science & Engineering, Chemistry, Mechanical Engineering or a related engineering and science discipline.  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.

SEM-3:  Composites with Tunable Thermal Properties

APG - Aberdeen Proving Ground, Maryland

Controlling heat flow in composite materials is of fundamental interest. In this project, the student will design, fabricate and test fiber reinforced composite materials (e.g., carbon fiber with a polymer matrix) and explore methods of significantly changing the material’s thermal properties (i.e., thermal diffusivity, thermal conductivity, thermal contact resistances). Students majoring in engineering, chemistry, physics or materials science are ideal for this project.

SEM-4:  Convergent Manufacturing Design for Printed Hybrid Electronics

APG - Aberdeen Proving Ground, Maryland

Convergent Manufacturing (CM) is the term used to describe fabrication of multifunctional devices and structures; and can incorporate any number of manufacturing technologies and materials to produce these products. CM is expected to revolutionize the design, fabrication and application of electronic packaging and antenna structures. An objective of this project to establish process-material-design relationships. The researcher will develop feedstock materials, including conductors, dielectrics, insulators, and manufacturing processes, such as aerosol deposition, inkjet printing, SLA, FFF, injection molding, robotic milling and drilling, and plasma modification, and manufacturing parameters to prepare the materials/parts.  The researcher will characterize the resulting materials using electronics characterization methodologies, such as conductivity measurements, mechanical characterization, such as tensile testing, thermal testing, such as dynamic mechanical characterization. 

SEM-5:  Polymer AM for Structural & Energetic Materials

APG - Aberdeen Proving Ground, Maryland

The primary focus of the researcher will be to formulate and additively manufacture high solids loaded resins and polymers for application to structural materials and energetic materials. The researcher would characterize the thermal and mechanical properties of the polymer via DSC, DMA, ASTM mechanical testing, rheology, and/or microscopy. The researcher would work within the team to transition the materials to others to enable energetic characterization of relevant material.

Depending on the student’s interest, aspects of the research can be to prepare and scale up chemical reactions and separations to produce monomers and polymerizable oligomers for light curing and thermal curing additive manufacturing techniques with DEVCOM-ARL expert chemists. The researcher would then characterize these chemicals using FTIR, NMR, and other techniques. 

SEM-6:  Rate-Responsive Composites

APG - Aberdeen Proving Ground, Maryland

Fiber-reinforced composites are highly advantageous because of their high strength-to-weight ratios. Designed for maximizing structural loading, composites often suffer from brittle fracture and a lack of durability. In this project, students will investigate materials used to improve impact durability, rate-dependent mechanical behavior, and fatigue lifespan. Ideal candidates are majoring in materials or mechanical engineering, have experience with CAD and MATLAB, and would enjoy hands-on experimentation. 

SEM-7:  Synthetic Composites

APG - Aberdeen Proving Ground, Maryland

Incorporating metals into organic media in a controlled fashion can be challenging. In this project, the student will design, synthesize, and analyze mixtures to develop methods for tuning concentration and homogeneity/gradient of metal content in polymers. Students majoring in chemistry, engineering, or materials science are ideal for this project.


Explores foundational concepts and builds cumulative capabilities to simultaneously address multiple axes of complexity for future Robotics and Autonomous Systems (RAS) operational concepts.


Sciences and applied research of weapon target interactions.

TE-1:  Injury Biomechanics

APG* - Aberdeen Proving Ground, Maryland

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.

TE-2:  Unmanned Systems Teaming Autonomy: Multiagent Control and Image-Based Autonomous Landing

ALC - Adelphi Laboratory Center, Maryland

Research and development in increasing levels of unmanned systems’ teaming autonomy including:

  • Multi-Agent Systems Operation: Coordinating diverse unmanned systems (e.g., quadrupeds, UAS, and UGS) from a central control point, like the Android Team Awareness Kit (ATAK) navigation app.
  • Seamless Communication: Facilitating effective collaboration and communication among multi-agent systems.
  • Advanced Autonomous Navigation: Enhancing autonomous navigation accuracy, particularly in vision-based landing for safer and more precise landings.


Internal, transitional, and external ballistics; launch, flight, control, and navigation of guided weapons and aerial systems; development of novel weapon concepts.

WS-1:  GNC Research with the Julia Programming Language

APG - Aberdeen Proving Ground, Maryland

The Julia programming language aims to solve the “two-language” problem by being as easy to write as python and as fast to run as C.  However, it is not widely used in guidance navigation and controls (GNC) communities.  Transitioning work in flight simulation, control theory, state estimation, image-based navigation, reinforcement learning, and other areas goes far beyond syntax differences.  We’re looking for candidates with strong coding and problem-solving skills to help us figure out how to do GNC research with this new tool.

WS-2:  Structural Refinement of Metal Alloy Systems

APG - Aberdeen Proving Ground, Maryland

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.

WS-3:  Synthesis of Metallic Nanoparticles via Emerging Plasma Technologies

APG - Aberdeen Proving Ground, Maryland

The student will perform plasma experiments for synthesizing and/or processing novel materials for army applications. Materials will focus on nanocomposites including metallic nanoparticles, nanodiamonds and other nanomaterials. Through this project, the student will also learn baseline materials characterization techniques, including X-ray photoelectron spectroscopy, scanning electron microscopy, gas sorption, particle size analysis and thermal analysis.


Appointment location varies. 

  • ALC – Adelphi Laboratory Center, Maryland
  • APG – Aberdeen Proving Ground, Maryland
  • ARL-WEST – Playa Vista, California

*Remote and/or Hybrid appointment considered on case-by-case basis.


U.S. Citizenship is not required to be considered for the Summer Student Experience.  Non-U.S. Citizens are strongly encouraged to apply early to allow additional processing time for the ORAU Immigration Office and ARL security.

Qualifications/Eligibility Requirements

  • Degree: Candidates should be in an academic program leading to an undergraduate or graduate degree; or hold one of the following: Associate's Degree, Bachelor's Degree, Master's Degree, or Doctoral Degree.
  • Minimum GPA: 2.5 at an accredited university or technical institute.
  • Age: Must be 18 years of age

Application Requirements

The following must be included with the online application:

  • Resume - Please list your relevant coursework and lab work on your resume as well as all papers, presentations, or publications you may have authored or co-authored. Include any reprints or abstracts if they are available.
  • Transcripts - Transcript verifying receipt of degree/or identifying current enrollment. Original student copies are acceptable.
  • Statement of Interest - Write a one-page description of your scientific research experience. Please include references to your lab work as well as any relevant academic coursework. How does this experience intersect with your personal and professional goals?
  • References - Reference forms are not required for the Summer Student Experience, but names and contact information for references are part of the online application. During the review process, some Advisors/Selecting Officials may contact references for further evaluation.

For questions about the Summer Student Experience Program, please email