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- NTNU Norwegian University of Science and Technology
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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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master’s degree with academic qualifications in digital health, data analysis, and/or machine learning applied to health research. Admission to the PhD program requires a 120 ECTS master’s degree, including
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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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-principles electronic structure calculations Perform materials screening including machine learning to identify promising thermoelectric materials for cooling technology The successful candidate is expected
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that includes material space construction and exploration, candidate selection and verification, providing data for machine learning models to optimise membrane properties, structure, and fabrication. The fellow
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applications for a PhD position focused on developing a theoretical framework for monitoring and updating adaptive learning systems (including machine learning/artificial intelligence systems) under formal
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will utilize economic theory, simulation, economic evaluation and machine learning to quantify the benefits of advanced diagnostic technologies in reducing overdiagnosis. Competence You must have
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well as innovative and inquiry-based teaching and learning. The Faculty consists of six departments as well as a Faculty administration. Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/296312/phd
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machine learning, distributed systems, and blockchain technologies. Your immediate leader will be the Head of the Cryptology Unit at the Department of Information Security and Communication Technology. The
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mechanistic process models with machine learning for accuracy, generalization, and interpretability. Uncertainty-aware AI: robust inference under noise, drift, and changing conditions; knowing when a model is