51 learning-"https:"-"https:"-"https:"-"https:"-"https:" "https:" "System Office" PhD positions in Norway
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16th October 2026 Languages English English English The Department of Engineering Cybernetics has a vacancy for a PhD Candidate in Hybrid Learning-Control Methods for Autonomous Underwater
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, research institutes, industry, public agencies, and leading global institutions. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early-stage
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in the group of Professor Rafi Ahmad. The Ahmad group is also part of the Norwegian Network for One Health Resistome Surveillance (NORSE), a national network of 13 Norwegian institutions. The group is
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25th September 2026 Languages English English English The Department of Marine Technology has a vacancy for a PhD Candidate in Deep Learning Enhanced FSI analysis of Modular Floating Structures PhD
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for probabilistic unsupervised learning for structured biological data. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/307053/3-years-phd-position-in-probabilistic-machine
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try again. UiO/Anders Lien 4th October 2026 Languages English English English 3-years PhD position in probabilistic machine learning and statistics Apply for this job See advertisement About the
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SINTEF, Equinor, and Total. The main objectives of the project include the development and the integration of signal processing and machine learning methodologies aiming to improve flow assurance via field
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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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inclusive community. Your immediate Line Manager will be the Head of Department. About the project The PhD position is a part of the Knowledge-building Project “Increasing Emergency Load Limits
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to characterize forests and their biodiversity. The research will focus on developing multimodal learning approaches that combine complementary forest information across data sources, spatial scales, and time. A