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) and deep reinforcement learning models Developing novel statistical models for uncertainty quantification, causality estimation, and prediction accuracy Publishing research in leading biomedical and
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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by enabling efficient parameter calibration and uncertainty quantification, making simulations more accurate and broadly applicable in engineering contexts. The PhD project focuses on developing
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to develop accurate models that capture the complexities of aging and material degradation. Furthermore, the project will focus on incorporating uncertainty quantification into the models to ensure