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classical fluid systems and the burgeoning field of active and driven matter and develop data-driven theoretical models for temporally correlated transport processes. The project is well-suited to current PhD
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neural networks, broadly defined. The successful candidates will have completed (or nearly completed) a PhD in applied mathematics, scientific computing or theoretical physics focusing on one or several
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