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to an exciting project at the intersection of data assimilation and optimal transport, focusing on Wasserstein Gradient Flows. The goal is to design innovative assimilation schemes using nonlinear approximation
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carbohydrates from different food matrices. Engaging on the optimization of methodologies for the analysis of novel quality markers, with special focus on mass spectrometry and nuclear magnetic resonance
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prior exposure to modern developments in at least one of these fields: functional analysis, numerical analysis of PDEs, mathematical foundations of deep learning, optimization, approximation theory
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