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utilizing existing and upcoming sensor deployments. The opportunity will include a range of activities and experiences including data collection, image analysis, workflow/algorithm development, sensor testing
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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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? Under the guidance of a mentor and as the selected candidate, you will learn how to apply algorithms to predict parts of coding and noncoding sequences for optimization. As a result, designed mRNA
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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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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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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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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
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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