33 machine-learning-"https:" "https:" "https:" "https:" "https:" scholarships in Norway
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to research, development and demonstration of a methodology for building and integrating machine learning solutions for past technical artefacts. Contributing to the development of holistic view of product
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health, epidemiology, statistics, biostatistics, or machine learning/artificial intelligence. You must have a strong academic background from your previous studies and have an average grade from your
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6th March 2026 Languages English English English The Department of Computer Science has a vacancy for a PhD Candidate in Modeling Edge AI Computer Architectures Apply for this job See advertisement
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hazards, enhancing asset protection, maritime security, emergency preparedness, and societal resilience. The project will leverage advanced AI and machine learning techniques to enable predictive risk
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the researchers from Department of Automation and Process Engineering will play a key role. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
(as machine learning techniques, etc.). Personal characteristics In the evaluation of which candidate is best qualified for the PhD position, emphasis will be placed on education, experience and
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systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI outputs in a known structure. This
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promise and peril of hybrid intelligence—humans and machines working and learning together. Our mission is to establish an internationally leading interdisciplinary hub that advances foundational research
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(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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selection criteria Experience with machine learning or other relevant AI technologies Experience with condition monitoring, preferably within maritime domains Knowledge of ship machinery and systems Good oral