Machine Learning for State Estimation and Decision Propagation

About the Research

ARL requires one full time engineering research scientist/engineer for a post-doctoral fellowship to support a program in Network of Networks to enable novel methods for agent learning, adaptation, and model distribution in highly heterogeneous environments where humans are coupled to machine decision agents. Specifically, the opportunity will develop novel theories, develop experiments to validate, and hardware to implement solutions to the problem of:

• Given the advances made by the Google DeepMind project (and others) how can we enable both hierarchical and deeply integrated Human-in-the-Loop (HIL) reinforcement, transfer learning for heterogeneous agents, and extend these methods from simulation demonstrations to hardware in the loop mixed systems?

About SEDD

The Sensors and Electron Devices Directorate (SEDD) is the Army’s principal center for research and development in the exploration and exploitation of the electromagnetic spectrum, which includes radio frequency, microwave, millimeter-wave, infrared (IR), visible, and audio regions. SEDD is responsible for advances in laser sources, RF sources, IR sensors, signature detection and decoding, target imaging and its interpretation, fusion of data derived from several sensors, and electromagnetic protection.

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.

Requirements

A recent graduate with a PhD in control systems, electrical, mechanical engineering, computer science, material science, mathematics, physics or other appropriate discipline.

Qualifications

This person will be expected to lead their own research efforts but participate within a highly collaborative research group. This person will be expected to publish first author efforts in peer reviewed literature; contribute technically to peer reviewed literature in diverse areas within and outside of the team; and, develop experimental and transition efforts across the team. This project is expected to research fluidly in python, Linux, ROS, Matlab, C/C++.

Skills

A deep understanding of various machine learning, transfer and reinforcement learning techniques, developing theories supporting multi-agent learning, and transfer of learned behavior across heterogeneous agents.

Benefits

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Pay

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Schedule

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