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Field
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science, data science, applied mathematics, physics, materials science, or a related field. Solid background in machine learning and/or computer vision. Interest in representation learning, active learning
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10 Mar 2026 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Electronic Systems Research Field Computer science Researcher Profile
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, Department of Computer Science, the Technical Faculty of IT & Design and the Center for Clinical Data Science (CLINDA) and Center for General Practice (CAM), Department of Clinical Medicine, the Faculty
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Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description
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, maintenance, and operation of engineering systems in order to reduce the frequency and consequences of failure. Vision We are seeking a PhD student who is motivated to rethink how manufacturing systems
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This is NTNU NTNU is a broad-based university with a
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-critical clinical contexts. Vision Healthcare systems are increasingly adopting algorithmic and AI-driven tools to support clinical decision-making. While these technologies promise improvements in
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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progress as an independent researcher Be prepared for changes to your work duties after employment. Required selection criteria You must have a relevant master’s degree in telecommunications, computer
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environmental quality of the built environment. Vision We are seeking a highly motivated PhD candidate to investigate the sustainability and biodiversity benefits of urban green space interventions. Working with