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networks for real-time, adaptive diagnosis. b) Uncertainty in Dynamic Environments: Runtime uncertainties require sophisticated risk modeling; we will employ Bayesian deep learning and deep reinforcement
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real-world field data. The central research question of this thesis is: How can Extreme Value Theory (EVT) and Bayesian Networks (BN) be coupled to build a predictive and dynamic model of NaTech risk
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Bayesian prediction models with uncertainty quantification for trustworthy personalized treatment decisions in the T-PRESS Evidence Ecosystem Framework”. The primary objective of the T-PRESS consortium is to
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. The development of offshore wind farms is essential for the energy transition, but it also raises questions about its impact on the marine environment. Each wind farm exerts different pressures on the environment
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, data-driven surrogates) are widely used to obtain fast, approximate predictions. A major scientific challenge is therefore to combine information from models of different fidelity levels in a principled
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Bayesian prediction models with uncertainty quantification for trustworthy personalized treatment decisions in the T-PRESS Evidence Ecosystem Framework”. The primary objective of the T-PRESS consortium is to
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, we do not know how the shape and flexibility of particles control their preferential sampling of turbulence. The main objective of the thesis is to optimize particle shapes and mechanical properties
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entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
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lattice orientation by EBSD or local chemical composition by EDX [1]. For instance, an original protocol based on Bayesian inference was recently co-developed by LEM3 and ICA to determine the single-crystal
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