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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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to achieve them. Acquire new knowledge quickly and use existing knowledge in new ways. Work constructively under pressure or in the face of adversity. Demonstrate strong problem-solving abilities with a
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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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in English (reading, writing, speaking). • Show ability to work independently as well as in a team. • Good knowledge in AI, machine learning, data science and mathematics. • Good knowledge in one
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to achieve them. Acquire new knowledge quickly and use existing knowledge in new ways. Work constructively under pressure or in the face of adversity. Demonstrate strong problem-solving abilities with a
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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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. 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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1 Aug 2026 Job Information Organisation/Company Wageningen University & Research Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile First Stage
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embed user-defined conditions and structural constraints directly into machine learning routines to achieve high-resolution 3D interpolation of rock mass properties. These synthesized geotechnical fields