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control algorithms (such as adaptive control or model predictive control) to reliably maneuver Army projectiles to the target despite limited state information, control authority, and changing flight
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
Organization National Aeronautics and Space Administration (NASA) Reference Code 0325-NPP-NOV25-JPL-Eng How to Apply All applications must be submitted in Zintellect Please visit the NASA
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control systems and thermal protection for on-board actuation systems. The candidate will have a deep knowledge of smart material based actuators, mechanisms, advanced control algorithms, and thermal
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hardware implementing Deep Reinforcement Learning algorithms for the tactical arena. Additionally, High Level Synthesis (HLS) will be incorporated to obtain hardware designs optimized for various criteria
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Fusion of information from heterogeneous sensors for robot missions Optimization of complex algorithms for computationally limited platforms Experimentation and validation methods in robotics Adaptive
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collection of streaming sensor data. This project focuses on utilizing state-of-the-art reinforcement algorithms to 1) dynamically learn from multi-agent actions and context, 2) evaluate the environment and
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engineering, computer science, or related fields Expertise in machine-learning and/or online BCI Advanced programming skills (i.e. Python, Matlab, R) and strong experience in algorithmic design, mathematical
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on the energy constraints associated with their mission planning and logistics of operation. The goal of this research is to develop new techniques and algorithms that can better plan the missions of aerial and