48 machine-learning "https:" "https:" "https:" "https:" "https:" PhD positions in Norway
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to SAFE. Delivering EVU course from SAFE center. Required selection criteria A PhD degree (or equivalent) in biometrics, information security, computer science, electrical engineering, or machine learning
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» Autonomic computing Engineering » Maritime engineering Technology » Computer technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 25 Apr 2026 - 23:59 (Europe
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
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24th April 2026 Languages English English English The Department of Electronic Systems has a vacancy for a PhD Candidate in Distributed Machine Learning Apply for this job See advertisement This is
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. Strong (inter-)national network in field of application. Experience with high-performance computing (HPC) and large datasets. Experience with machine learning applied to geophysical signals. Experience in
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Computer science » Computer systems Computer science » Programming Technology » Communication technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 26 Apr 2026 - 23
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25th February 2026 Languages English English English The Department of Materials Science and Engineering has a vacancy for a PhD Candidate in machine learning and large language models (LLMs
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for machine learning models to optimise membrane properties, structure, and fabrication. The fellow will play a key role in the experimental part of the project, including: Preparation and characterisation
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(s) Number of offers available1Company/InstituteDepartment of Computer ScienceCountryNorwayCityGjøvikPostal Code2815StreetTeknologivegen 22Geofield Contact City Gjøvik Website http://www.ntnu.no Street
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the semantic foundation that enables AI systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI