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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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, partial differential equations and scientific computing, to name a few. There are competing LC theories e.g., molecular-level models with molecular-level information, mean-field models that average
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model due to the mathematical challenge of solving the multiple partial differential equations simultaneously. With the support of the combined sponsorship from the university and industrial partner
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in numerical techniques used to solve ordinary and partial differential equations and be proficient with related commercial or open-source software tools (e.g., ANSYS, FEniCS, OpenFOAM or similar) and