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flow theory or machine learning frameworks (e.g., PyTorch, TensorFlow). Strong written and oral communication skills in English. Personal characteristics To complete a doctoral degree (PhD), it is
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
for the position. Preferred selection criteria Experience with machine learning or other relevant AI technologies Scandinavian language skills Previous experience from industry or research in engineer-to-order
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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
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engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 2 Oct 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Is the job
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
(ph.d.) in artistic development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning
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15th October 2026 Languages English English English The Department of Energy and Process Engineering has a vacancy for a PhD Candidate in Integrated Energy Systems for Sustainable Digital
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selection criteria Knowledge/experience with control engineering, information fusion and/or data assimilation, marine technology Knowledge of and hands-on experience with machine learning and/or statistical
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candidate for a full-time (100%) PhD position for 3 years. You will join the research group Power Electronics and Electrical Machines (PEM) at IEL, where we foster an open, inclusive, and collaborative
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. We are seeking a candidate motivated to explore the how emerging technologies – such as machine learning, generative AI, and extended reality (XR) – impact societal preparedness planning required
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? To address these questions, the PhD candidate will develop and evaluate new decision-support methods that combine optimization and machine learning. Particular attention will be given to methods that can