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prediction to process optimization. The focus of this PhD project is to develop and apply machine learning methods across three interconnected tasks: 3D microstructure characterisation. The student will
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. There will be a strong focus on developing robust methods with mathematical guarantees, focusing on statistical and optimization properties with potential applications to cardiology and medicine. The exact
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, such as heterogeneity of data sources and communication constraints. By leveraging tools from statistical signal processing, machine learning, optimization, and mathematical modeling, the project aims
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. Optimal transport is a key mathematical concept that allows us to understand notions like inference and sampling as dynamic processes of probability distributions. Building on the theoretical insights, we
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical