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background in computer science, chemistry, physics, materials science, or a related field. Experience with deep learning frameworks, preferably in the context of generative models. Experience with atomistic
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computing. The position offers an exciting opportunity to join a cutting-edge research project focusing on the co-design of CMOS and spintronic devices for next-generation energy-efficient and non-von-Neumann
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account for molecular interactions in electrolyte solutions, including complex chemical reactions; (2) to enable real-time CO2 storage process simulations by optimizing the framework’s computational
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if the process overall runs most efficiently with high tar load before the POX or if a low tar load is preferred. Modelling (chemical engineering models, computational fluid dynamics and/or models with detailed
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learning Distributed and federated training The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another
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computational challenges. This PhD project will investigate novel registration strategies for ultrasound-derived musculoskeletal point clouds. The work will focus on developing geometry-aware alignment methods
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of wind turbines. Despite remarkable progress in structural health monitoring boosted by AI, purely data-driven models have no physical interpretability and poor generalization capabilities. Thus
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to be) enrolled at one of the faculty’s master programmes in Physics. Students on the integrated programme will enroll as PhD students simultaneously with completing their enrollment in this MSc degree
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to contribute to the groups ongoing work on integrating environmental issues into macroeconomic models with the purpose of providing an assessment of the financial stability and physical risks given
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complete, a master’s degree in computer science, mathematics, statistics, IT, engineering, biotechnology, bioinformatics, economics, or a related field. Experience with programming in Linux environments