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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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emphasizes principled modeling, reproducible experimentation with open datasets and simulations, and publication-ready contributions targeting leading venues in machine learning and wireless communications
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reflection interpretation (2D regional and 3D blocks), potential field modelling, and plate reconstructions. The total duration of the Ph.D. position is three years, starting by August 1st 2026. The PhD
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might alternative or hybrid solutions provide advantages? The candidate is expected to take initiative to narrow the research scope toward concrete applications. Prototype information models and
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Participate in the research group “Engineering Geology and Rock Mechanics” and FME RenewHydro (https://www.ntnu.edu/renewhydro ) Participate in international activities such as conferences Contribution in
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to evolve advanced, human-centered AI technology to empower human learning, including designing, developing and evaluating systems and models to enhance learning through AI technology. The PhD fellow will
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on secure and trustworthy data sharing. The development and refinement of maritime AI models depend on access to large amounts of high-quality operational data. However, data sharing across organisational and
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tools often fail to predict stability issues due to model inaccuracies and the uncharacterized dynamics of PECs, which are typically proprietary and not fully disclosed by manufacturers. Efforts
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solutions provide advantages? The candidate is expected to take initiative to narrow the research scope toward concrete applications. Prototype information models and demonstrators are expected to be
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2026-2030. There will be approximately 25 doctoral scholarships and some postdoc positions. More information can be found here: https://www.ntnu.edu/mai Duties of the position Complete the doctoral