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DEVCOM Army Research Laboratory

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Heterogeneous Operating in Dynamic and Unstructured Environments DEVCOM Army Research Laboratory in United States

Degree Level

Postdoc

Field of study

Computer Science

Funding

Available

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Country

United States

University

DEVCOM Army Research Laboratory

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Keywords

Computer Science
Electrical Engineering
Mathematics
Computer Vision
Game Theory
Reinforcement Learning
Sensor Fusion
Collaborative Robotics
Robotics
Autonomous Navigation
Multi-robot Systems
ML

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About this position

This opening is part of the DEVCOM Army Research Laboratory Research Associateship Program and sits within the Science of Intelligence Systems (SIS) area. The project focuses on computational methods for robots operating in dynamic, unstructured environments, with an emphasis on enabling machines to perceive and understand their surroundings, move and orient themselves, and collaborate in complex missions with limited human intervention.

The research is explicitly aimed at Army-relevant robotics, so the technical challenge is to build methods that are highly efficient, robust, agile, and able to perform under constraints on computational power, storage, and bandwidth. The advertised research directions span autonomous behavior and perception for single- and multi-robot systems, including navigation in operational settings, object detection and tracking from stationary and moving platforms, GPS-denied localization, semantic reasoning, heterogeneous sensor fusion, and distributed decision-making.

Other highlighted themes include collaboration among heterogeneous robot teams, mixed-initiative operations, adaptive sampling for decision-making, human-robot collaborative tasks, and learning-based approaches such as reinforcement learning and graph neural networks. The opportunity is therefore well suited to applicants interested in robotics, autonomy, multi-agent intelligence, and algorithms that must work reliably on constrained platforms in realistic missions.

The listed ARL advisors are Carlos Nieto, Jeffrey Twigg, and Sean Gart.

Funding details

Available

What's required

Applicants must have a bachelor's, master's, or doctoral degree, with eligibility listed for associate’s, bachelor’s, master’s, or doctoral academic levels depending on fellowship category. Preferred qualifications include experience with robotics hardware such as sensors, actuators, and motors; familiarity with computer vision and image processing; knowledge of AI and autonomous systems; proficiency in Python and C++; experience with MATLAB; and familiarity with ROS 1 or ROS 2 for building, deploying, and debugging robotic systems.

How to apply

Apply through the Zintellect application portal using the opportunity page. Submit a CV or resume, three reference forms, and transcripts with proof of degree if applicable. If selected by an advisor, prepare and submit a research proposal to the ARL-RAP review panel.

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