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, methods and applications. The areas represented include: fluid mechanics, biomechanics, statistics and data science, computational mathematics, combinatorics, partial differential equations, stochastics and
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combining the mathematical and computational cultures, and the methodologies of statistics, logic and machine learning in unique ways, Integreat's machine learning will solve fundamental problems in science
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computer science or statistics A solid background in mathematics, linear algebra and statistics. Documented experience with Bayesian spatiotemporal modelling, including experience with the INLA framework
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represented include: fluid mechanics, biomechanics, statistics and data science, computational mathematics, combinatorics, partial differential equations, stochastics and risk, algebra, geometry, topology
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and society. Integreat draws on the research strengths of researchers and students from the departments of Mathematics, Informatics, Philosophy, and the Oslo Centre for Biostatistics and Epidemiology
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the mathematical and computational cultures, and the methodologies and theories, of statistics, logic, language technology, ethics and machine learning, in new and unique ways. The focus of Integreat is to develop
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with respect to academic credentials. Qualification requirements: Master’s degree or equivalent in computer science or statistics A solid background in mathematics, linear algebra and statistics
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is a new initiative that organises the local bioinformatics community and drives bioinformatics innovation by integrating computational and biological sciences to address complex life science
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of Mathematics and Natural Sciences. IFI is Norway’s largest university department for general education and research in Computer Science and related topics. The Department has more than 1800 students on bachelor
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computational and biological sciences to address complex life science challenges. It fosters collaborations and develops advanced computational tools through a hub for multi-omics and systems biology. Project