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- University of Oslo
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- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- University of Agder
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Field
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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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: Applicants must hold a master's degree or equivalent education in applied physics or mathematics or a relevant engineering subject (e.g. mechanical, aerospace, chemical, or process safety). Master students can
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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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, health 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
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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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of Informatics (IFI) is one of nine departments belonging to the Faculty of Mathematics and Natural Sciences. IFI is Norway’s largest university department for general education and research in Computer Science
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. Qualifications and personal qualities Applicants must hold a master's degree or equivalent education in Informatics, Physics or Mathematics, or must have submitted his/her master's thesis for assessment prior
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in Quantum Technology at the Department of Informatics . The position is for a fixed-term period of 3 years with the possibility of a 4th year with career-promoting work (e.g. teaching at
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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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there is a premise for employment that the PhD Research Fellow is enrolled in USN’s PhD-program in Technology within three months of accession in the position. About the PhD-project Offshore wind energy