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tracking error. The aim is decision-grade uncertainty quantification (UQ) and principled data-driven parameter selection. Hence, the project will develop automatic portfolio rebalancing driven by UQ analysis
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record relevant to career stage research expertise in one or more of the following: digital twins, cyberphysical systems, human-centred AI causal modelling, uncertainty quantification, explainable AI
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of multi-fidelity and active learning strategies for molecular systems. The candidate will collaborate in an international research team on related research questions in machine learning, uncertainty
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and parameter estimation, Uncertainty quantification and model calibration, Mathematical modelling of biological or physical systems, Machine learning and deep learning theory, Spatio-temporal and
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of online/continual learning and uncertainty quantification into converter and drive decision-making under real-time constraints. Depending on the specific position and background, the work may include drive