Participating Lab: DEVCOM Army Research Laboratory (DEVCOM ARL)

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.

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.

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.

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).

3D Printing, known as Additive Manufacturing (AM), is transforming the manufacturing industry. Army Research Laboratory (ARL), the nation’s premier laboratory for land forces, is in the frontier of advancing innovative AM technologies and their applications. ARL is creating great strides in AM science and technology (S&T) to solve the Army challenges and mission readiness. Effective numerical and experimental methods for quantifying properties of the AM parts during the printing process are critical for enabling AM applications. Understanding the relationships between processing, microstructure, and properties of these material is critical to drive development of future metallic feedstock alloys. Alloys designed specifically for AM will give enhanced performance over today’s state of the art, and enable the Soldier to adapt to evolving threats by manufacturing qualified components at the point of need while reducing logistical footprints.
Candidates should demonstrate a strong academic background with research experience in the following areas: additive manufacturing of metallic materials; numerical modeling of processes and materials; microstructure-processing-property-relations; powder processing (milling, sizing, compaction and sintering); machine learning and data analytics; and mechanical testing (static and dynamic) of materials. Additional experience with metallography (specimen preparation, optical and electron microscopy) and metallurgical characterization (x-ray diffraction, SEM, TEM, micro-CT, etc.) are highly desirable.

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.

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

The Army demands significant performance from electronics, ranging from operating in extreme environments (>1000 F) to demanding extreme efficiency (nW). In this research experience you will be testing the next generation of materials and devices for operating in these challenging conditions.

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.

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.

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.

Radiovoltaics aims to develop devices and systems that can offer persistent power that can deliver for months to years without resupply or recharging, but research in materials and devices is a key part for enabling this technology. This research encompasses radiation physics, electron beam irradiation, semiconductor materials, device physics, materials chemistry, and radiochemistry towards the goal of developing a radiovoltaic with high power output and operational lifetime.

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.

This project will address the evaluation and optimization of dissimilar metal laminates for robust mechanical performance in extreme environments. Techniques such as laser impulse welding, explosive bonding, roll bonding, and others can be used to produce dissimilar metal laminates with novel mechanical properties at various length scales. However, materials selection-process parameter-joint microstructure relationships are not conclusively established, and their effects on joint performance across extreme service temperatures, loads, and strain rates are not fully understood.

Participants will characterize and test as-received, as-welded, and heat treated specimens to inform weld process maps and microstructure-performance relationships. Techniques including finite element modeling (FEM), heat treatment, metallographic sample preparation (sectioning, grinding, polishing, etching), optical microscopy, electron microscopy, quasistatic mechanical testing, and laboratory impact (Charpy V-notch, drop tower) testing may be used to generate, collect, and analyze data during the project. 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.

Project Overview
This project introduces an undergraduate student to computational semiconductor physics through the modeling and empirical validation of diamond-based transistors. Diamond is a leading wide-bandgap material for next-generation power electronics due to its superior thermal conductivity, high breakdown electric field, and high carrier mobility. Working in COMSOL Multiphysics, the student will bridge theoretical device physics with practical, data-driven simulation methods.

Research Plan
The student will begin by onboarding with the COMSOL Semiconductor Module, constructing and simulating a standard silicon baseline transistor to master mesh generation, boundary conditions, and current-voltage (IV) characteristics.

Building on this foundation, the student will modify the device geometry to match target diamond transistor dimensions and transition material properties from silicon to diamond. Once the diamond model converges and operates without numerical errors, the student will calibrate critical material parameters—such as carrier mobilities and contact resistances—against measured experimental data.

Deliverables & Skills Gained
By summer’s end, the student will deliver a fully calibrated, predictive simulation model and present their findings. The student will gain hands-on expertise in COMSOL Simulation, advanced semiconductor materials, and experimental data integration.

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.

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.

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.

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.

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.

The objective of this work is to explore new electrolyte and electrode materials to improve the performance of Lithium-ion batteries. Materials may be developed to increase the durability of Lithium-ion batteries for use in extreme temperature environments. Performance under high rate of charge and discharge may be enhanced by developing and studying new material compositions. The energy density and safety may also be improved by developing new cathodes or anodes which operate at higher voltage or have higher capacity for lithium insertion. The performer will synthesize new materials, fabricate new formulations, build test coin cells and measure electrochemical performance. Battery galvanostatic charge and discharge cycling, voltammetry, and material synthesis methods may be utilized. Other physical characterization may also be done including elemental or structural analysis. The expected outcome is a better understanding of materials that can improve the performance of Li-ion batteries for Army uses.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

Lithium-ion batteries are critical to the success of Army missions and security operations. However, extreme environments present major operational challenges, including electrolyte instability, reduced capacity, shortened lifespans, and the risk of thermal runaway. To overcome these vulnerabilities, this project develops highly concentrated electrolytes, advanced anodes and cathodes, and cells engineered for high-voltage (HV) and high-temperature (HT) performance. These new battery materials will be evaluated in full cells to analyze their electrochemical properties and novel chemistries under extreme conditions (~5V and >80°C).

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.

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.

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.

Are you interested in exploring the physical hardware technologies that will power the future of microelectronics?

Next-generation ferroelectric devices, including Ferroelectric Field-Effect Transistors (FeFETs) and Ferroelectric Capacitors (FeCAPs), are at the forefront of solid-state research, offering transformative potential for low-power non-volatile memory and neuromorphic hardware.

Because characterizing these emerging technologies is a multifaceted challenge, this research opportunity is designed to be flexible. Depending on your academic background, technical strengths, and interests, your project will focus primarily on one or more of the following core areas:

1) Experimental Testing & Hardware Setup: Collaborating with research staff to develop, configure, and execute custom electrical test protocols across various wafer probe stations.

2) Data Analysis & Visualization: Designing and implementing automated Python or MATLAB pipelines to process, analyze, and visualize large, high-throughput experimental datasets.

3) Device Physics & Modeling: Fitting experimental laboratory data to physical models using numerical tools to assist with parameter extraction and help explain underlying device behavior.

The Universal Expectation: Regardless of which technical track your project focuses on, reproducibility and clear communication are key. You will be expected to author comprehensive technical documentation, detailed code comments, or standard operating procedures for the specific workflows, scripts, or test setups you develop during the summer.

This approach ensures a tailored, high-impact summer experience that aligns with your career goals, whether you are leaning toward experimental laboratory engineering, scientific computing, or device physics.

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.

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.

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.

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.

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.

In this project, students will be helping design Verilog logic for different Army chip design efforts, including AI hardware and digital signal processing. Students will design digital circuits in Verilog, write testbenches for those designs, and simulate those testbenches using Verilator and Cadence Xcelium. The updates and designs students work on will be used in future ARL chip designs.

Students will work in the lab and help in developing testing protocols for digital chips designed by ARL researchers. Students will test for functionality and performance (correctness, power, speed, etc.). Students will also work to debug chip functionality problems should they arise. Chips are tested both through an FPGA interface, and through the NI PXIe board.

The participant will help to add new functionality to the front-end software for the Field Programmable Neural Array (FPNA) chip developed by ARL. They will work on the FPNA Synthesis Tool (FST), and 1) upgrade the functionality to work with new FPNA hardware, 2) add additional features to the software, 3) expand the functionality of the software to work on more Open Neural Network eXchange (ONNX) neural network layers.

Research opportunities exist for modeling highly transient, coupled, multiphysical fields in solids using analytical, finite element, finite difference, peridynamic [1,2], phase field [3], discrete element, and other computational methods. Emphasis is placed on model validation with experiments, and verification with analytical solutions to transient coupled-field boundary value problems involving wave propagation, thermo-mechanical and electromagnetic fields in finitely deformed matter [4], fluid-structure interaction associated with shock, impact, dynamic fracture [5], and fragmentation phenomena. Additional research opportunities exist in the areas of machine learning, topology optimization [6], multiscale modeling of material microstructures using genetic programming and computational geometric methods [7] for use in conjunction with large-scale finite element and other computational solvers.

References:

[1] Gazonas GA, Aksoylu B, Wildman RA, “Fast Fourier transform-based solutions of initial value problems for wave propagation in microelastic media,” Journal of Mechanics of Materials and Structures, Vol. 19(1),61– 89, 2024.
[2] Aksoylu B, Gazonas GA, “On the choice of kernel function in nonlocal wave propagation,” Journal of Peridynamics and Nonlocal Modeling, Vol. 2, 379–400, 2020.
[3] Chua J, Agrawal V, Breitzman T, Gazonas GA, Dayal K, “Phase-field modeling and peridynamics for defect dynamics, and an augmented phase-field model with viscous stresses,” Journal of the Mechanics and Physics of Solids Vol. 159, 2022.
[4] Weile DS, Hopkins DA, Gazonas GA, Powers BM, “On the proper formulation of Maxwellian electrodynamics for continuum mechanics,” Continuum Mechanics and Thermodynamics, Vol. 26, Issue: 3, 387–401, 2014.
[5] Zhang G, Gazonas GA, Bobaru F, “Supershear damage propagation and sub-Rayleigh crack growth from edge-on impact: a peridynamic analysis,” International Journal of Impact Engineering, Vol. 113, 73–87, 2018.
[6] Gaynor AT, Johnson TE, “Eliminating occluded voids in additive manufacturing design via a projection-based topology optimization scheme,” Additive Manufacturing, Vol. 33, 101149, 2020.
[7] Wildman RA, Gazonas GA, “Multiobjective topology optimization of energy absorbing materials,” Structural and Multidisciplinary Optimization, Vol. 51, 125–143, 2015.

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.

Solid-liquid phase change materials (PMCs) have quickly become a very active area of research in transient thermal mitigation, enabling smaller, more compact thermal systems that rely on high thermal loads over short periods. Liquid metals have shown promise in high power pulsed electronics due to their high thermal conductivity and ability to quickly absorb energy in response to pulsed loads. Often, thermophysical properties for these materials are poorly understood, only describing a single phase or lacking information on important materials of interest.

Participants will aim to understand and characterize PCMs of Army importance, including liquid metal and organic phase change materials using a variety of characterization tools here at ARL. Methods including steady-state and transient interrogation using optical, electrical, and thermal techniques will be used to determine thermal conductivity, diffusivity, and heat capacity of materials of interest. You will learn to develop materials, build experimental techniques, and interpret results for a wide variety of materials to be used to better understand the PCM design space.

Participants are expected to create a poster and present on their results at the end of the program, be able to answer questions on their research, and understand the mechanics behind thermal measurement techniques and materials.

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.

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.

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.

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.

This project aims to deploy recently developed capabilities to simulate deflagration of solid composite propellants (SCPs) in the DoD HPC and test its ability to model grain-scale propellants. Current capabilities rely on phenomenological behavior to capture combustion dynamics, while newly developed model employs heat solvers. Although this results in an increase in computational cost, the benefit of physics-informed dynamics could outweigh these costs. Furthermore, SCPs are a common choice for solid rocket motors due to their chemical and mechanical stability. Understanding the dynamics of the burn process at the microscopic and mesoscopic length-scales is essential to better predicting the propellant’s performance. The intern will be tasked with ensuring that the models are properly configured by developing a workflow that generates synthetic microstructures and simulates the corresponding deflagration.

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.

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

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.

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)

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.

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.

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

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.

To support decentralized operations in contested logistics environments, this research addresses the logistical burden of fuel transport by developing point-of-need power generation and fuel synthesis. The objective is to design, characterize, and optimize novel, highly selective catalysts and reactor architectures that drive energy-intensive reactions portably and safely at ambient temperatures and pressures.
Participants will engage in hand-on, interdisciplinary research at the intersection of electrochemistry, reactor engineering, and materials science. Specifically, the participant will engineer catalyst slurry formulations, fabricate gas diffusion electrode flow cells, and/or configure photocatalytic UV reactors. To analyze these systems, they will employ advanced electrochemical methods alongside analytical quantification techniques including spectrophotometry and gas chromatography-mass spectrometry (GC-MS).
Additionally, participants will correlate electrochemical performance with structural properties using advanced diagnostic tools like Powder X-Ray Diffraction (PXRD), Transmission Electron Microscopy (TEM), and X-ray Photoelectron Spectroscopy (XPS). The expected outcome of this opportunity is the generation of validated catalyst performance datasets, the refinement of reactor standard operating procedures, and the creation of a technical summary report and research poster for presentation at the summer symposium.

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.