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requires admission to the PhD programme at the Faculty of Mathematics and Natural Sciences. The application to the PhD programme must be submitted to the department no later than two months after taking up
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leading to the successful completion of a PhD degree. The fellowship requires admission to the PhD programme at the Faculty of Mathematics and Natural Sciences. The application to the PhD programme must be
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algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the methodologies of statistics, logic and machine learning in unique
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for this job See advertisement About the position Position as PhD Research Fellow in Machine Learning is available in the Department of Informatics and the Norwegian Centre of Excellence Integreat . Starting
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to the successful completion of a PhD degree. The fellowship requires admission to the PhD program at the Faculty of Mathematics and Natural Sciences. The application to the PhD program must be submitted no later
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Position as PhD Research Fellow in formal methods for data protection in digital twins is available at the Department of Informatics. Starting date no later than December 1, 2025. The fellowship period is
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of Mathematics and Natural Sciences. The application to the PhD program must be submitted no later than two months after taking up the position. For more information see: http://www.uio.no/english/research/phd
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the course of the period of employment Francesco Saggio/UiO via Unsplash Francesco Saggio/UiO What skills are important in this role? The Faculty of Mathematics and Natural Sciences has a strategic ambition to
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Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the methodologies
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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