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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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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
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and secondments. • Blended Learning Approach: Our training combines intensive in-person workshops at partner institutions with regular interactive online seminars, journal clubs, and research
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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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demonstration of a methodology for building and integrating machine learning solutions for past technical artefacts. Contributing to the development of holistic view of product lifecycle, its digital artefacts
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Technology (NTNU) for general criteria for the position. Desired qualifications Applicants should possess a basic understanding of key AI concepts (machine learning, neural networks, prompt engineering, human
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broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
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is linked to the new research center FME RenewHydro . You will join the research group Electrical Machines and Electromagnetics (EME) at IEL, where we foster an open, inclusive, and collaborative
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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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, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling. The centre has numerous collaborations with leading biomedical research groups internationally and in
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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