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First Stage Researcher (R1) Positions PhD Positions Application Deadline 1 Oct 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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To complete a doctoral degree (PhD), it is important that you are able to: Work independently Work in a structured way, set goals and make plans to achieve them Present and discuss your research with other
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of Marine Technology at NTNU has a vacancy for a PhD Candidate in Deep Learning enhanced FSI modelling of Multi-modular Floating Structures. The position is part of the AIMOS project (Artificial Intelligence
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well as a wise use of machine learning methods. The candidate should hold an MSc degree in Applied Physics, Electrical Engineering, or the equivalent. The PhD position is firmly embedded in a physics
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
of Mechanical and Industrial Engineering is offering an exciting PhD position within the Norwegian Maritime AI Centre , a national centre with a focus on applied artificial intelligence (AI) in the maritime
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MSCA Doctoral Network position “Electric solar wind sail mission design” We are now looking for two (2) Doctoral Researchers. This PhD topic is part of the “Electric solar wind sail doctors” (E
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societal mission in research and education. If you think this position is relevant and interesting, we encourage you to apply, regardless of gender, functional ability, and cultural background, or whether
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based on physics-informed neural networks and transfer learning. The PhD candidates will have the opportunity to be affiliated with the program BRU21 , with the IoT@NTNU, and with the Norwegian Open AI
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that qualifies you for a further career in higher education and research, in and outside academia. Your immediate leader will be the Head of Department. About the project The PhD position is internally funded by
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-source scientific software library. You will be part of a larger project at the Uncertainty in Complex Systems lab (PI: Dr Max Hinne) aimed at learning which statistical and/or computational model is most