218 machine-learning "https:" "https:" "https:" "https:" "https:" positions in Sweden
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analysis, statistical modelling, linear mixed models, and machine learning among others. The position is well suited for an individual interested in quantitative genetics and data analysis that wishes
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conditions, and administrative and technical support, among other benefits. See more information at: https://www.umu.se/en/department-of-computing-science/. You will research in collaboration with
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learn to combine modern analysis techniques like Morawetz estimates with Penrose's Nobel prize winning geometrical insights and formalisms, intricate symmetry operators, spinor techniques and powerful
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computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our dedicated webpage . About the research project
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computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our dedicated webpage . About the research project
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, such as pulse design or numerical optimization Background in data-driven or machine-learning approaches relevant to optimal control (e.g., model learning, reinforcement learning) What you will do Take
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an interest in Bayesian statistics, applied probability theory, computational mathematics, machine learning, and generative AI, and offers the opportunity to contribute to a rapidly growing research field with
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experimental platform and combine it with continuum modeling of complex materials and machine-learning-based analysis methods to understand and predict biofilm structure and growth. Supervision: Shervin Bagheri
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to the forefront of quantum technology, and to build a Swedish quantum computer. Building a quantum computer requires a multi-disciplinary effort involving experimental and theoretical physicists, electrical and
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, undergraduate and postgraduate education in communications engineering, statistical signal processing, network science, and decentralized machine learning. Welcome to read more about us at: https://liu.se/en