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project “ALPS - AI-based Learning for Physical Simulation”. Expected start date and duration of employment This is a 2–year position from 1 October 2025 or as soon possible. Job description You will be
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MagTense framework, and will be used in powering future beyond-state-of-the-art simulations within magnetism. Qualified applicants must have: PhD degree in physics, astronomy, engineering, computer
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publications stating the scientific publications on which the applicant wishes to rely Copy of PhD diploma - if PhD diploma has not yet been received, please include statement from your supervisor A motivation
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PhD degree in civil, mechanical, or electrical engineering, computer science, or a closely related field. A desire to engage in cross-disciplinary research at the intersections of civil, mechanical
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Curriculum vitae Master’s degree and PhD degree or equivalent (copy of original + verified English translation) A complete list of publications Reference letters/references may also be included A statement of
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and PhD degree certificates or equivalent (copy of original/official English translation). Complete list of publications. Publications most relevant to the position. Summary and documentation
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simulate wind and solar forecast uncertainties on pan-European level, leveraging latest machine learning weather forecast models Apply machine learning methods to forecast day-ahead and balancing market
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coil patterns and core geometries, to reduce coil loss in HPMCs You will also contribute to teaching and supervising BSc and MSc student projects, and be co-supervisor for PhD students. Anyone who: has a
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-resolved micro and nano CT data. Develop and apply numerical implementations in simulation studies as well as to large experimental micro and nano CT data. Explore multi-modality reconstruction techniques
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on the application material, and an application must include: Motivated application. Curriculum Vitae. Master’s and PhD degree certificates or equivalent (copy of original/official English translation). Complete list