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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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, or using climate modeling such as the Norwegian Earth System Model to constrain models and observations. Your immediate leader will be the Head of Department. About the project The PhD candidate will join
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are offering a fully funded PhD position in the field of Physics-informed Learning-based Control. This interdisciplinary research area bridges control theory, machine learning, and physics-based modeling
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23 Aug 2025 Job Information Organisation/Company SINTEF Department Digital Research Field Computer science All Researcher Profile First Stage Researcher (R1) Recognised Researcher (R2) Established
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Country Norway Application Deadline 12 Oct 2025 - 23:59 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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. Collaborative opportunities with clinical and computational research teams will also be available. The successful candidate will use a robust and physiologically relevant 3D spheroid model, closely replicating
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Technology » Energy technology Environmental science Computer science » Modelling tools Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 31 Oct 2025
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Full-time Hours Per Week 37,5 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
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approach of data-driven membrane discovery that includes material space construction and exploration, candidate selection and verification, providing data for machine learning models to optimise membrane
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of smart technologies to visualize yard operations in a digital form (such as virtual models and digital twins). Smart technologies can collect, analyze, and represent data from various sources
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access. The goals of such access include supporting registry operations as well as health care research. Of particular interest in this context are differentially private algorithms for statistical model