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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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, including electrical engineering, control theory, industrial engineering, electronics engineering, energy policy, data science, and applied mathematics. As part of the Alliance program, your project will be
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systems Documented knowledge in power and energy markets preferably Nordic and European markets Documented knowledge in dynamic modelling of power system Knowledge of mathematical optimization. Knowledge
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degrees in either the natural sciences (chemistry, physics, mathematical/computational biology) or in the formal sciences (statistics, computer science, mathematics), but must have a serious interest in
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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mathematics. A MSc degree in mathematics or a related field with strong mathematical content must be obtained before the starting time. Other desirable skills (but not required): Background in quantum computing
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structured and mathematical mind-set, and a self-motivated drive to excel in research. Prior experience with research in photonic crystals, band topology, or symmetry analysis is an advantage. Only candidates
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supervision of BSc and MSc student projects. Contributing to the teaching at the department to get teaching experience. Participating actively in the research community, including attending and presenting
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the BSc and MSc levels. Present and disseminate research findings at both national and international scientific conferences. You must have a two-year master's degree (120 ECTS points) or a similar degree
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Professor Jørgen Ellegaard Andersen and Associate Professor Shan Shan at QM. Candidate Profile Required Candidates should have a strong foundation in mathematics. A MSc degree in mathematics or a related