26 natural-language-processing-phd PhD positions at NTNU Norwegian University of Science and Technology
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students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position We are seeking a highly motivated PhD candidate
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application process here. About the position The Department of Materials Science and Engineering (IMA) at the Natural Science Faculty, has a vacancy for a position as PhD candidate related to modelling
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Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 22 Jan 2026 - 23:59 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per Week 37,5 Is the job funded
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member of the Norwegian Public Service Pension Fund (SPK) Free Norwegian language training at a basic level (A2) As a PhD Candidate at NTNU, you will have access to employee benefits . Diversity Diversity
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NTNU and the application process here. About the position The Department of Mechanical and Industrial Engineering has a vacancy for PhD Candidate in Antagonistic threats-aware Digital Twins in product
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NTNU and the application process here. About the position The Department of Mechanical and Industrial Engineering has a vacancy for PhD Candidate in Ant Digital Twins for food safety. This is a 3-year
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position A three-year PhD position in computational condensed matter physics is
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students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position A PhD position, fully funded for three years
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position A three-year PhD position in theoretical condensed matter physics is
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NTNU and the application process here. About the position The aim of this PhD project is to develop explainable physics-informed RNNs for autonomous navigation and neural observer design within