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guidance throughout the postdoctoral term. The appointed postdoc will be part of the faculty research group KITE , which conducts research on knowledge work, collaboration and learning with emerging
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term. The appointed postdoc will be part of the faculty research group KITE , which conducts research on knowledge work, collaboration and learning with emerging technologies in higher education and in
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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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meet the requirements for admission to the faculty's doctoral programme in Engineering Cybernetics . Strong programming skills, in particular Python, and practical experience with modern machine learning