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motivated PhD candidates that, combining model-based (physics) and data-driven (machine-learning) approaches, will develop innovative, highly accurate and highly efficient solvers for rarefied gas flows
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knowledge exchange and learning communities between different fields of sensitive settings through dissemination and international network activities. This PhD project aims to make several contributions
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