Heterogeneous operating in dynamic and unstructured environments

About the role

Heterogeneous operating in dynamic and unstructured environments
Robust perimeter defense systems
Robotic autonomy in mixed-initiative operations
Collaboration of heterogeneous robot teams in communications-limited environment
Autonomous navigation at operational tempo
Detection and tracking of moving objects from stationary and moving robots
Multi-robot object tracking, classification and recognition
GPS-denied localization of robots and objects in the scene
Reasoning over semantic concepts
Fusion of information from heterogeneous sensors for robot missions
Optimization of complex algorithms for computationally limited platforms
Experimentation and validation methods in robotics
Adaptive sampling of information in decision-making
Decision-making algorithms for human-robot collaborative tasks
Game theory applied for multi-agent learning
Distributed optimization communication for collaborative multi-robot tasks
Multi-robot multi-task using Graphical Neural Networks (GNN)
Multi-robot navigation using reinforcement learning techniques

About ARL-RAP

The Army Research Laboratory Research Associateship Program (ARL-RAP) is designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. Scientists and Engineers at the CCDC Army Research Laboratory (ARL) help shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs by pursuing scientific research and technological developments in diverse fields such as: applied mathematics, atmospheric characterization, simulation and human modeling, digital/optical signal processing, nanotechnology, material science and technology, multifunctional technology, combustion processes, propulsion and flight physics, communication and networking, and computational and information sciences.

About SCIENCE OF INTELLIGENCE SYSTEMS (SIS)

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

A complete application includes:

• Curriculum Vitae or Resume
• Three References Forms
• Transcripts

Qualifications

• Experience with robotics hardware (e.g., sensors, actuators, motors).
• Familiarity with computer vision and image processing techniques.
• Knowledge of AI and autonomous systems.
• Proficiency in Python and C++.
• Experience with MATLAB.
• Ros 1 or Ros 2 for robotics applications, including building, deploying, and debugging robotic systems.
• Discipline(s): Chemistry and Materials Sciences (12 ), Communications and Graphics Design (2 ), Computer, Information, and Data Sciences (17 ), Earth and Geosciences (21 ), Engineering (27 ), Environmental and Marine Sciences (14 ), Life Health and Medical Sciences (51 ), Mathematics and Statistics (11 ), Physics (16 ), Science & Engineering-related (2 ), Social and Behavioral Sciences (29 )
• Degree: Bachelor's Degree, Master's Degree, or Doctoral Degree.
• Academic Level(s): Associate’s Degree (Journeyman Fellow), Bachelor’s Degree (Journeyman Fellow), Master’s Degree (Journeyman Fellow), or Doctoral Degree (Postdoctoral Fellow).

Application Process

If selected by an advisor the participant will also be required to write a research proposal to submit to the ARL-RAP review panel for :

• Research topic should relate to a specific opportunity at ARL (see Research Areas)
• The objective of the research topic should be clear and have a defined outcome
• Explain the direction you plan to pursue
• Include expected period for completing the study
• Include a brief background such as preparation and motivation for the research
• References of published efforts may be used to improve the proposal

Contact Information

Questions about this opportunity? Please email ARLFellowship@orau.org.