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Aeromedical Evacuation (AE) medical crews. The goal of the project is to embed interactive software algorithms, derived from Aeromedical Evacuation Clinical Protocols (AECPs), directly into the provider's
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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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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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to participate and gain knowledge in researching existing topography, wave, water level, ecological, and sediment transport data from the laboratory and/or field to validate existing models and test algorithm
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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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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