37 postdoc-computational-fluid-dynamics-"Prof" Fellowship positions at UiT The Arctic University of Norway
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18 Sep 2025 Job Information Organisation/Company UiT The Arctic University of Norway Department Department of Physics and Technology Research Field Computer science Mathematics Engineering
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First Stage Researcher (R1) Positions Postdoc Positions Country Norway Application Deadline 5 Oct 2025 - 23:59 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per Week 37,5 Is the job
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Chemistry » Molecular chemistry Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Country Norway Application Deadline 29 Oct 2025 - 23:59 (Europe/Oslo) Type of Contract Temporary Job
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sciences » Psychology Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Country Norway Application Deadline 15 Oct 2025 - 23:59 (Europe/Oslo) Type of Contract Temporary Job Status
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four years. The nominal length of the PhD programme is three years. The fourth year is distributed as 25 % each year and will consist of teaching and other duties relevant for the Department
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(MRI). The research will be of practical relevance to current societal challenges related to education and health. The C-LaBL Mentorship Program will provide a comprehensive training scheme to a
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The position A PhD position is available
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Profile Recognised Researcher (R2) Positions Postdoc Positions Country Norway Application Deadline 28 Sep 2025 - 23:59 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per Week 37,5 Is
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8 Sep 2025 Job Information Organisation/Company UiT The Arctic University of Norway Department Department of Clinical Medicine Research Field Medical sciences Computer science Ethics in health
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the group's research on developing novel machine learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image