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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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The position will be attached to the project “SURE-AI” (http://www.sure-ai.no/ ) funded by The Research Council of Norway, and lead by Simula Research Laboratory. The main focus of the research will be
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. Applicants must have excellent skills in oral and written English. Fluency in a Scandinavian language is an advantage. All our staff is given the opportunity to attend courses in order to learn Norwegian and
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to the IE faculty's Doctoral Programme (https://www.ntnu.edu/ie/research/phd/), see Section 6-1 of the PhD regulations for more information. You must have a relevant Master's degree in Computer Science and
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, knowledge, beliefs, abilities, etc. The project will be conducted within the Logic and Artificial Intelligence (LAI) research group (https://www4.uib.no/en/research/research-groups/research-group-for-logics
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Norwegian University of Life Sciences (NMBU), within the Faculty of Science and Technology(REALTEK), invites applications for a PhD position in Applied Causal Machine Learning. The position is affiliated with
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, specialising in either machine learning or quantum information science. The Department of Information Theory at Simula UiB conducts research on two main areas: statistical learning theory and quantum information
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environments that aims to address how distributed sensing, fibre-optic monitoring, environmental observations, drone- and satellite-based data, operational infrastructure datasets, and/or machine learning can be
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principles for BGC expression and to support iterative AI-guided design–build–test–learn cycles. The precise scope of the doctoral project will be refined according to the candidate’s qualifications, the BGCs