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Are you fascinated by application-oriented research in mathematics and eager to work at the interface of numerical optimization, optical design, and uncertainty quantification? In this PhD project
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. The candidate will work at the intersection of climate resilience assessment, life-cycle modelling and uncertainty quantification. The successful candidate will collaborate with three research groups within
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stress fields); design computational pipelines that integrate image-based anatomy, blood flow physics, and uncertainty quantification; validate the developed methods on retrospective multi-center clinical
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imaging methods that are more robust and computationally efficient, while also providing a natural framework for uncertainty quantification and experimental design. A central question is whether suitable
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is to obtain imaging methods that are more robust and computationally efficient, while also providing a natural framework for uncertainty quantification and experimental design. A central question is
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estimation of 3D coronary hemodynamics (velocity, pressure, and wall shear stress fields); design computational pipelines that integrate image-based anatomy, blood flow physics, and uncertainty quantification
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techniques from mathematical modelling, machine learning, uncertainty quantification, distributed decision-making, or other data-driven approaches. Your duties and responsibilities in this 4-year project
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is decentralized or only partially observable? Depending on the research direction, you may employ techniques from mathematical modelling, machine learning, uncertainty quantification, distributed
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statistical learning theory and probabilistic models; prior exposure to notions of robustness, resilience, or uncertainty quantification is an advantage. Mathematical maturity and experience with formal
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Computational Fluid Dynamics (CFD) has become indispensable for aerospace design, many important problems—including high-fidelity flow simulations, uncertainty quantification, and multidisciplinary optimization