Participating Lab: DEVCOM Army Research Laboratory (DEVCOM ARL)

Laminated composites plays an important role for the Army across many applications due to their high strength-to-weight ratios and design flexibility. These materials are widely used in personnel protection, sabots, and UAVs. However, characterizing the mechanical properties of these materials remains a challenge, particularly due to their anisotropic behavior.
This project focuses on the implementation of inverse methods based on the Virtual Fields Method or VFM to enhance the identification of material properties of laminated composites. VFM allows for the extraction of mechanical parameters from measured displacement/strain fields from Digital Image Correlation (DIC) obtained during experimental testing. By applying this approach, we will develop a robust framework that integrates experimental data with computational modeling.
The outcomes of this research will not only enhance the fundamental understanding of laminated composites but also provide valuable insights for the design of safer and more efficient composite structures across multiple industries.

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

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

The student will be involved in the culturing and genetic engineering of filamentous fungal organisms. This will involve designing experiments and using modern cloning and transformation techniques. The student will then work on characterizing the modified organisms they create to see if there are functional changes due to the genetic modifications.

This summer internship project will develop skills related to understanding and utilizing fundamental structure processing property relationships in steel. The project will integrate modeling-informed experimental design with applied heat treatments and characterization. Technical training on new tools and techniques will be provided throughout the summer internship and professional development opportunities and experiences within DEVCOM ARL and across APG.

Computer science has often borrowed different things from neuroscience to use as a blueprint for artificial systems. For example, convolutional neural networks (CNNs) are used for tasks like image classification, feature recognition, and object identification. However, the brain uses many other systems to interact with the world that can serve as foundations for new machine learning algorithms. This project will explore different brain systems and how these systems can be used to develop new, energy-efficient, adaptable algorithms. Potential research questions include: How can different neuroscience research be used as inspiration for new intelligent systems? What and how can technology benefit from different brain-inspired systems?

AI is a quickly evolving tool, that when used properly, can assist both soldiers and civilians alike. But, AI is not yet able to adapt as efficiently or as effectively as humans. We (humans) are highly adaptable and can adjust to a wide variety of situations quickly and without any additional training. On the other hand, current AI systems need large amounts of situation-specific training to become effective and useful, and when the situation changes, it can completely confuse the system. This project will look at the creation and modification of human-guided adaptation approaches; a method that uses the human to inject adaptability into intelligent systems, reducing training time, cost, and errors. A few potential research questions include: How can humans intuitively adapt intelligent systems for new uses, environments, and situations? How can intelligent systems take in and use human feedback and experience?

Students will work on Li-ion battery materials to improve battery performance, including energy density, capacity, life cycle, and stability.

The project involves continually assessing the design of sinuous antennas and exploring new designs and approaches for application to standoff synthetic aperture radar (SAR) and close-in downward-looking ground penetrating radar (DLGPR) for ultra-wideband (UWB) linear and nonlinear radar transceivers.

The project aims to provide updated capabilities to the transceiver via hardware and firmware upgrades. The upgrades will involve inserting signal and image processing into the RFSoC to support pre-screening, signal ID, and direction finding, along with spectrum agility and reconfiguration based on electromagnetic environment (EME) assessments.

This project aims to hire one student to create a demonstration of an ARL-developed AI chip performing speech recognition. The AI chip is designed using a 12nm finfet process and has state-of-the-art AI inference efficiency. The student will connect the AI chip development board to a microphone input to capture speech commands and an output screen to display the commands correctly. The development board will also be connected to source meters to show power consumption of the speech inference. The student will be located at DEVCOM ARL headquarters in Adelphi, MD. The project mentor will be remote but visit regularly, and other team members will work at DEVCOM ARL headquarters.

DEVCOM ARL is interested in scientific research into integrating active, actuating soft materials into passive structures for robotic maneuverability. The goal is to obtain multi-degree of freedom motion by leveraging additively manufactured complex architectures coupled with selective positioning of active components.

Join our innovative research team for physiological data for multi-modal LLMs.
What You’ll Do:
Assist in developing and prototyping a multimodal dialogue system that integrates physiology and speech/text to improve understanding between LLMs and humans.
Conduct experiments to evaluate the effectiveness of our system by comparing it to traditional models without physiological data.
Data sources may include gaze, gestures, pupil size, heart rate, and movement data.
Why You Should Apply:
Work at the cutting edge of robotics and cognitive science with a focus on real-world applications in military settings.
Gain hands-on experience in natural language processing, machine learning, and human-machine interfaces.

Technologies like Virtual Reality and Augmented Reality (collectively XR) create new opportunities to help people understand complex, quickly changing, and highly uncertain information. Research under this project will help us understand how to effectively and efficiently portray information to people and groups to support fast, accurate decision-making, creativity, and adaptability.

Specific topics might include:
Dynamic filtering, contextual zoom, and information summarization for individual and group awareness
Uncertainty portrayal and expression
Interacting with new kinds of information
Human/Autonomy Interaction in XR

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, creating mutant strains, and evaluating their performance under different conditions. You’ll collaborate with experienced researchers to design, test, and refine microbial strains to achieve specific, innovative outcomes.

We seek a driven student to join our team in a computational biology project focused on writing scripts to automate data workflows and streamline experimental planning. You will help develop and implement scripts that bridge input and output formats for robotic systems and create complex data 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 seek a chemistry 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) and high-performance liquid chromatography (HPLC) to analyze and identify chemical compounds produced by engineered strains. This project will give you hands-on experience in sample preparation, operation of MS and HPLC 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 seek a detail-oriented student to join our team in a research project focused on laboratory automation, where you’ll work with advanced robotic systems to streamline and optimize experimental workflows. This project uses state-of-the-art automation platforms such as the Tecan Fluent, Echo robot, Mantis robot, and Integra Assist Plus to perform tasks like high-throughput screening, liquid handling, and assay setup with precision and efficiency. You’ll gain valuable experience in programming and operating these automated systems to accelerate data collection and improve experimental reproducibility, working closely with researchers to develop cutting-edge solutions in a variety of scientific fields.

We are looking for motivated students to join a unique project where teams will compete to stress-test strain development using innovative new pipelines. This project is designed to simulate real-world challenges in strain engineering, pushing newly developed microbial strains to their limits under various conditions. You will work in a collaborative, yet competitive, environment, designing experiments, troubleshooting strain performance, and utilizing advanced pipelines for testing. Throughout the project, you will analyze data to assess which team’s approach yields the most robust and efficient strains, gaining valuable experience in experimental design, strain optimization, and teamwork.

We are looking for an enthusiastic student to join our team in a research project focused on protein engineering, where you will work to design and modify proteins to enhance their functions or create new capabilities. This project will involve using techniques such as mutagenesis, directed evolution, and computational modeling to develop proteins with desirable traits for applications in areas like biotechnology or industrial processes. You’ll gain hands-on experience in protein expression, purification, and characterization, working closely with researchers to test and optimize new protein designs.