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around the central theme of probabilistic methods for foundation models and world models: making them uncertainty-aware, calibrated, robust and useful for scientific decision-making, by tackling
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. Simulation-based inference (SBI) addresses this by training neural networks, such as flow-matching generative models, on simulated events. The project aims to develop efficient, robust and calibrated SBI
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