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Field
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Gaussian process regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty
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stability prediction Collaborating closely with experimental researchers for iterative model refinement Publishing high-impact research at the intersection of AI and molecular design Supervising students
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 12 hours ago
carbon-cycle modeling. The project will build a unified modeling framework that uses GEDI LiDAR and Landsat/HLS data to train deep learning models capable of predicting forest structure variables such as
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into model-predictive control (MPC) or reinforcement learning (RL) frameworks to compute optimal exoskeleton assistance in real time. Validating the developed methods in human experiments using motion capture
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experience or strong interest in machine‑learning approaches for accelerating molecular modelling and the prediction of chemical properties. For informal enquiries, please contact Dr Martin‑Martinez