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to create knowledge for a better world. You will find more information about working at NTNU and the application process here. ... (Video unable to load from YouTube. Accept cookie and refresh page to watch
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Project summary Recent advances in artificial intelligence (AI) have fuelled the idea of building “AI scientists”, i.e., systems that can autonomously perform part or all of the research process
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autonomy: learning-based systems that can operate safely over long horizons, respect physical constraints, and provide predictable closed-loop behaviour. By grounding reinforcement learning in stability
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using signal changes to learn about the weather and take appropriate action. By combining AI with physics and real-time data, the project improves weather forecasts and makes communication systems more
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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method
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- or part-time research programme. This includes current doctoral researchers in the College of Arts and Law. The funding will be available from September 2026. One scholarship is available offering
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process here. ... (Video unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position Knowledge representation languages (KRL) can help to
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as well as published in leading subject and high-impact publications. The post-holder will have the opportunity to teach. Applicants should hold a PhD/DPhil in atmospheric physics, physics, data
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performance limits from the start. This project develops a pioneering methodology for data-driven optimization of next-generation material systems. You will bridge advanced computational mechanics with physical