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infrastructure for high-performance computing, AI and data that provides state-of-the-art services to more than 7,000 researchers as well as other collaborators. We operate the Arrhenius mid-range EuroHPC system
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development in powder bed electron beam additive manufacturing (EB-PBF), with a focus on AI-driven process planning, heat simulation, and process optimization. The work includes developing physics-based AI
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inherently spatio-temporal, i.e. physical processes around us evolve over both time and space, making spatio-temporal processes and data omnipresent in science and technology, with applications ranging from
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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essentially corresponding knowledge in another way. You should have an excellent knowledge of physics, especially quantum mechanics, thermodynamics and solid-state physics, preferably a master's thesis related