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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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uncertainty quantification for robust structural design, particularly for complex aero-engine systems with limited experimental data. Recent work by the University of Southampton developed a novel data driven
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machine learning-based electrochemical-thermomechanical (ECTM) model capturing effects of material anisotropy on cell swelling across the scales with uncertainty quantification Experimentally-validated
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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
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the fields of uncertainty quantification, data assimilation and optimisation under uncertainty, complementing data-driven approaches such as physics-informed machine learning. We will start by focusing
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mechanical engineering, physics, applied mathematics or a closely related subject. Interests on: Structural mechanics and dynamics, Stochastic modelling and uncertainty quantification, understanding