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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and
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simulation software related to radiation transport and computational fluid dynamics. Conduct performance profiling of existing scientific software to identify bottlenecks and implement strategies for improving
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) and Computational Fluid Dynamics (CFD), for polymer composite manufacturing processes Perform multi-physics simulations involving coupled thermal, mechanical, and material behavior across multiple
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APPFL and OmniFed. Major Duties and Responsibilities: Conduct and publish original research focused on data readiness methodologies and frameworks for scalable AI applications across fluid dynamics
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: Experience in one more of the following areas: Mathematical methods for kinetic and/or fluid equations Multiscale problems and model reduction Modern machine learning software tools and frameworks