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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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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