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, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will have experience in one or more of these subject
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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
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