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place to study and work. Postdoctoral Research Fellow position within Physics-Informed Machine Learning for Offshore Wind At the Department of Mathematics , there is a vacancy for a postdoctoral research
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applications for a PhD Research Fellow position in fairness in artificial intelligence, available at the Department of Informatics, in the Scientific Computing and Machine Learning (SCML) research group
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, interaction design, Human-Computer Interaction, human-centred AI, UX research, immersive or interactive media, and other related areas. Teach and supervise topics in the Bachelor’s and Master’s programmes in
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intelligence, available at the Department of Informatics, in the Scientific Computing and Machine Learning (SCML) research group. Starting date as soon as possible/by agreement. The fellowship period is three (3
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or environmental engineering, Mathematics (Operations research) or Computer Science or Machine Learning). Documented knowledge of relevant methodologies, both quantitative and/or qualitative, at master’s level
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Recognised Researcher (R2) Positions Postdoc Positions Application Deadline 23 Sep 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Is the job funded through the EU
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applications in image analysis and machine learning, as well as in digital signal processing and acoustic imaging. There are about 20 Postdocs and PhD research fellows in the group with financing from a variety
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
for the position. Preferred selection criteria Experience with machine learning or other relevant AI technologies Scandinavian language skills Previous experience from industry or research in engineer-to-order
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, specialising in either machine learning or quantum information science. The Department of Information Theory at Simula UiB conducts research on two main areas: statistical learning theory and quantum information
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mathematical structures and computational algorithms underlying modern machine learning and artificial intelligence. Relevant themes include geometric and algebraic methods for learning, structure-preserving