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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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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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uncertainty quantification. Addressing these shortcomings is a research challenge in both core machine learning methodology and the application domain. This project will tackle both in close collaboration with
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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