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be focused on learning how to develop algorithms, performing biochar characterization tests, and characterizing microbial communities that colonize biochar in different ecosystems. Learning Objectives
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validate algorithms for estimating heat stress, counting animals, and estimating mass in real-time. Although research is needed to integrate One Health assessments across the soil-pasture-animal continuum
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to develop novel statistical techniques, analyze satellite and other remote sensing data, implement machine learning algorithms, assess numerical model performance, improve risk assessment tools, and deepen
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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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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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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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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