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Field
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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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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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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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tools (e.g., drones, 3D mapping) for high-resolution geological mapping and rock mass quality assessment. Develop and calibrate numerical models using field data and case studies to simulate various
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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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that balance energy supply and demand, improve electricity grid flexibility, and optimize renewable energy integration. These solutions contribute to climate change mitigation by reducing emissions and to
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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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mathematician (doctoral student). Karlsson’s group is part of the Division of Optimization and Systems Theory within the Department of Mathematics at KTH. Currently, a large focus of the group is to develop