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experiments using high-level programming languages (e.g., Python, MATLAB, R, or Julia). Curate and integrate experimental data to calibrate and validate models, including parameter estimation and uncertainty
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: (i) the rarity of extreme events, which renders classical statistics inadequate; (ii) the uncertainty inherent in cascading effects; and (iii) the lack of confrontation between numerical models and
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such as scalable identification algorithms, uncertainty quantification, and the integration of learning-based models with formal verification. We offer a supportive, inclusive, and collaborative research
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fields at a manageable computational cost. You will be exposed to surrogate (AI/ML) approaches to accelerate micro-to-macro links, as well as uncertainty quantification to account for variability in
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estimators. Provide uncertainty quantification of the resulting estimators. Deploy the results developed in the first stage for linear value-function estimation problems in reinforcement learning theory. Lay
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advanced AI-enabled methods for uncertainty analysis and quantification to support high-fidelity IoE digital twins. The research will investigate approaches for identifying, modelling, and integrating
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supported by the SAILING project and will focus on developing advanced AI-enabled methods for uncertainty analysis and quantification to support high-fidelity IoE digital twins. The research will investigate
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statistics. We are looking for a motivated candidate, with a deep interest in mathematical statistics, with a view towards developing new methods for uncertainty quantification. Starting date no later than
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, or demonstrable projects in computer vision, multi-modal ML, digital twins or biomedical ML. Familiarity with uncertainty quantification and model explainability methods. Strong software engineering practices: Git
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better subsurface understanding, uncertainty quantification, and robust forecasts suitable for emissions reduction, increased energy efficiency, and recovery improvements with large amounts of data