224 associate-professor-computer-"https:" "https:" "https:" "https:" "OsloMet storbyuniversitetet" PhD positions in Norway
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Positions Application Deadline 28 Feb 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a
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27 Feb 2026 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Electronic Systems Research Field Computer science Researcher Profile
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position, please contact Associate Professor Marie Haugli-Sandvik, email: marie.h.sandvik@ntnu.no or Rune Volden, email: rune.volden@ntnu.no . If you have any questions about the recruitment process, please
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if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme (https://www.ntnu.no/studier/phet) You must
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grade background, you may be considered if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme: https
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position, please contact Associate Professor Marie Haugli-Sandvik, email: marie.h.sandvik@ntnu.no or Rune Volden, email: rune.volden@ntnu.no. If you have any questions about the recruitment process, please
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programme at the University of Oslo (UiO). The fellows will be supervised by professors at the Departments of Social Anthropology and of Health and Society. The two dual degree candidates will also be
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meet the requirements for admission to the NV faculty's Doctoral Programme https://www.ntnu.edu/nv/phd Competence in programming (e.g. Python or similar). Good oral and written presentation skills in
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This is NTNU NTNU is a broad-based university with a technical-scientific
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cookie and refresh page to watch video, or click here to open video) About the position Distributed machine learning takes advantage of communication and distributed computing to utilise distributed data