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alloys), and additive manufacturing to push performance boundaries. The research will seek optimal trade-offs between compactness and performance, delivering foundational insights into the future of high
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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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Superconductors" at the Institute of Metallic Materials (IMW) offers a PhD position (m/f/div) in the field of superconducting dynamos Main tasks: The central research task is to investigate and optimize the use
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linkages based on numerical simulations and to transform them into AI- and ML-ready information to develop and implement an indirect inverse optimization framework to identify microstructures that exhibit
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into neural networks. PINNs can model real-world signals with sparse, non-uniform, and noisy data. A key question is determining the optimal method for integrating physical priors into neural networks
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doctoral schools in the natural sciences and one in the humanities and social sciences as well as numerous smaller research training groups. Advising The consulting team of the Graduate Academy’s Service
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utilise numerical techniques including the finite element method to describe biofluid flow and deformation in the human brain tissue. Parameters are inferred from clinical data including medical images
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optimal conditions for their successful doctoral degree and beyond. Doctoral researchers at GS SimTech: Have at least two supervisors from different subject areas Benefit from a second, external reviewer
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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, and more efficient operations. After all, the greenest energy is the one that’s not spent – and this project aims to unlock just that by refining the way we design and optimize airfoils. The focus