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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 hour ago
Organization National Aeronautics and Space Administration (NASA) Reference Code 0006-NPP-MAR26-GSFC-EarthSci How to Apply All applications must be submitted in Zintellect Please visit the NASA
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | about 1 hour ago
Organization National Aeronautics and Space Administration (NASA) Reference Code 0046-NPP-MAR26-LRC-EarthSci How to Apply All applications must be submitted in Zintellect Please visit the NASA
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 hour ago
Organization National Aeronautics and Space Administration (NASA) Reference Code 0310-NPP-MAR26-GSFC-EarthSci How to Apply All applications must be submitted in Zintellect Please visit the NASA
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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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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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advanced actuators, smart materials, sensor systems, and new adaptive control methods to manage the open-ended degrees of freedom of morphing systems. Methods will span both active smart-material based
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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