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human-natural systems modeling is desirable. Additionally, emerging advancements in control and machine learning (e.g., deep reinforcement learning) are of interest but not required. Ideally, candidates
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early as July 1st, 2024, in the areas of Machine Learning (ML)/deep learning theory and Natural Language Processing with a focus on ML/deep learning. Other areas of expertise in Artifical Intelligence (AI
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as early as July 1st, 2024, in the area of Robotics with a particular focus on Artificial Intelligence (AI) and Machine Learning (ML) applied to physical robot systems. The successful candidates must
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should be passionate about research at the interface between Biology and Physics, be willing to learn new skills beyond their expertise, and to be part of a collaborative lab environment. To apply
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, emerging advancements in control and machine learning (e.g., deep reinforcement learning) are of interest but not required. Ideally, candidates would have a Ph.D. in water resources engineering, hydrology
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paradigms based on complex network theory and approximated network models, risk analysis methods, and a combination of simulation and advanced machine-learning techniques for Urban Technology, Transportation