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Field
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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and cost outcomes, which include but are not limited to Statistical (risk) modelling, Model calibration and uncertainty estimation, Causal learning for explainable machine learning, Transparent
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Information https://www.mos.ed.tum.de/en/afs/career/our-jobs/planning-uncertainty-1/
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: You will study the effects of privacy-preserving machine learning on memorisation, fairness, interpretability and model uncertainty utilising techniques from information theory and quantitative
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