Army Educational Outreach Program (AEOP) Internships and Fellowships provide paid civilian research opportunities for high school through postdoctoral individuals in U.S. Army Research Labs and Centers. Projects focus on the most current and cutting-edge priorities of the Department of Defense.

Recruiting postdoctoral researchers at the U.S. Army Combat Capabilities Development Command Army Research Laboratory, known as DEVCOM ARL, the Army’s research laboratory. Nested strategically within DEVCOM and the Army Futures Command, ARL’s mission is to Operationalize Science. A hallmark of ARL’s mission is collaborative partnerships to broaden Army access to expert talent and accelerate transitions of science-enabled capabilities. This site operates on a rolling application basis. Due to the high volume of applications, not all candidates may receive an immediate response. However, all applications will remain under consideration for future opportunities as they become available. Applicants must be 16 to apply.

Postdoctoral fellows wanted for the following projects:

Machine Learning for Energetic Materials (APG006)

Researchers will have the opportunity to investigate R&D of artificial intelligence and machine learning (AI/ML) methods to be applied to problems for energetic materials (explosives and propellants).

Additive Manufacturing of Highly Filled Systems (APG007)

The primary focus of the researcher will be to formulate and additively manufacture high solids loaded resins and polymers for application to structural or energetic materials. The researcher would characterize the thermal and mechanical properties of the polymer via DSC, DMA, mechanical testing, rheology, and/or microscopy.
Depending on the candidate'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.

Data Science and Machine Learning applications to Cyber Security (APG008)

Machine Learning (ML) and data science have become integral parts of many domains (e.g., image analysis, networking protocols, network security, etc.), resulting in increased motivation for applications to cyber defense tools. Furthermore, the rapid rate of attacks and the immense volume of data significantly increase the demand on a small number of human analysts. This necessitates the use of data science and ML techniques to enable scalability and reduce the demand on human analysts. However, there are many challenges in the successful use of data science and ML for cyber security problems. Increasingly, supervised learning relies on a significant amount of quality labeled data. To avoid the requirement for a significant amount of labeled data, it is necessary to innovate semi-supervised methodologies in a resource-constrained domain for network communications in the cyber domain. In the network/communications domain, machine learning-based classifiers are generally trained within a closed environment. Specifically, datasets used for training and evaluation are static and do not vary. Conversely, network environments are dynamic over time. Adversaries' attacks become more sophisticated and change in response to defenders' actions, requiring a defender to retrain a classifier to reflect the new attacks in the intended environment for deployment.
This research is focused on data science and ML applications to network traffic (i.e., network traffic analysis, network forensics). Example key research questions include the following:
• How do we design ML-based network traffic classifiers using a limited amount of data?
• How do we leverage ML for network traffic classifiers in a resource-constrained environment?
• How can we apply ML to network forensics problems?

Composite Fabrication and Analysis (APG009)

This opportunity focuses on providing students with a foundational background in fabricating and characterizing fiber-reinforced composites to evaluate their material properties. Students will engage in the production of these composites using various resin transfer molding techniques. They will also gain significant experience in data reduction and model development using tools such as MATLAB, Python, COMSOL, ABAQUS, and/or Excel to correlate fabrication parameters with performance outcomes. Throughout this opportunity, students will gain hands-on experience in thermal and mechanical characterization through methods like differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and tensile testing. Additionally, students will have access to microscopy techniques, including scanning electron microscopy (SEM) and micro-computed tomography, to analyze the microstructure and surface characteristics of the composites. Further training opportunities in techniques such as mercury intrusion porosimetry, pycnometry, X-ray diffraction, electron spectroscopy, and wet chemistry synthesis/purification will also be available. This hands-on project is expected to provide valuable experience in composite fabrication, materials characterization, and data-driven analysis, effectively preparing students for careers in the composites industry.

Materials and Tool Development for Neuroscience Research (APG010)

This project involves the design and investigation of the mechanical, electrical, biomagnetic, and functional properties of various materials and model tools that will be used for neuroscientific study of tissues such as brain, bone, or skin. The topic contains a broad range of sub-tasks throughout areas of neuroscience, biomedical engineering, materials science, and biochemistry. Example duties may include construction and characterization of materials simulating tissue; investigation of various materials and techniques for appropriateness; design and developing physical models and/or techniques for constructing physical models; use of CAD for model development; or use of additive manufacturing for molds, models, or components. Tasks can range from theoretical development to practical application. A niche can be carved out based on knowledge and interest.

Neuroscience and Neurotechnologies (APG011)

Computer science has often borrowed different things from neuroscience to use as a blueprint for artificial systems. For example, convolutional neural networks (CNNs), are modeled after the mammalian visual system, and are used for tasks like image classification, feature recognition, and object identification. But there are many other systems the brain uses 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?

Hybrid Human-Technology Intelligence (APG012)

Most problems benefit from teamwork. Different mindsets, approaches, experiences, and strengths enable teams to accomplish large goals that would be impossible for a single individual to accomplish alone. As technology continues to advance, more and more teams will include both humans and AI agents. This project looks at how to integrate humans and machines to create hybrid teams that surpass what humans can accomplish alone. A few potential research questions include: What new forms of thinking emerge from combining human collectives with technology in novel ways? How might we accelerate the process of collective decision making or creative problem solving with novel frameworks, systems, and technological integration?

Human-Guided System Adaptation (APG013)

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?

Injury Biomechanics (APG014)

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.

To apply for a position:
1) Click Apply Now
2) Create a New Account
3) Start “2025 Fellowship Application”
4) Under “4. Fellowship Opportunities,” search for the opportunity using the AEOP reference code
5) Select to apply