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DEVCOM Army Research Laboratory

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Post-Doctoral Fellowship: Machine Learning for State Estimation and Decision Propagation 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
Mechanical Engineering
Electrical Engineering
Mathematics
Transfer Learning
Python Programming
Reinforcement Learning
State Estimation
Robotics
Statistics
Control System
Physics
Multi-agent System
ML

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About this position

This postdoctoral fellowship at DEVCOM Army Research Laboratory focuses on machine learning for state estimation and decision propagation in highly heterogeneous environments where humans are coupled to machine decision agents.

The project is framed around a Network of Networks research program and aims to develop novel theories, validation experiments, and hardware implementations for human-in-the-loop reinforcement learning, transfer learning, and the transfer of learned behavior across heterogeneous agents. The work asks how recent advances in deep reinforcement learning can be extended from simulation demonstrations to hardware-in-the-loop mixed systems.

The fellow will join a highly collaborative research group within Sensors and Electron Devices Directorate (SEDD), an Army research center working across electromagnetic-spectrum technologies, sensor systems, data fusion, and related areas spanning physics, electrical engineering, computer science, and materials-related research. The role combines theory, experimentation, and implementation, with expected research activity in Python, Linux, ROS, Matlab, and C/C++.

The position is designed for an early-career researcher who can lead an independent research effort while contributing technically to team-based work and publication goals.

Funding details

Available

What's required

Applicants must have a doctoral degree received within the last 60 months. The posting specifically seeks a recent graduate with a PhD in control systems, electrical engineering, mechanical engineering, computer science, material science, mathematics, physics, or another appropriate discipline. Candidates should be able to work independently, contribute in a highly collaborative research group, publish first-author peer-reviewed papers, and develop experimental and transition efforts. Experience with machine learning, transfer learning, reinforcement learning, multi-agent learning, and programming in Python, Linux, ROS, Matlab, and C/C++ is expected or strongly preferred based on the project description.

How to apply

Apply through the Zintellect opportunity page. Submit a CV or resume, three reference forms, and transcripts with proof of degree receipt. If selected by an advisor, prepare and upload the required research proposal through the link provided later in the process.

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