The recent breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML) demonstrate new promises for decision making in complex spatiotemporal environments. While deep learning has shown tremendous success in these domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training and inference of such models. We seek exceptional postdoctoral candidates hosted at UCSD Computer Science & Engineering. The candidate would be developing physics-guided machine learning to learn from spatiotemporal data such as time series and trajectories for various applications.

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