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headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You can find more information about working at NTNU and the application process here
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or in a legal process. Similarly, your ongoing employment is conditional upon the satisfactory maintenance of all relevant clearances and background check requirements. If you do not meet these conditions
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Innovation Leads Research Fellows (RFs) and Research Engineers/Assistants (RE/RAs) in the end-to-end development and productisation of solutions, including experimental design and prototype validation. Lead
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-Informed Neural Networks (PINNs) and hybrid models that respect the physical laws governing the real-world system Applying Deep Reinforcement Learning (DRL) algorithms to optimize processes within simulation
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should comply with the following requirements: i) a concluded bachelor’s degree in Information Technologies or a similar area; ii) Knowledge in the field of Human-Computer Interaction (information
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career researchers, and contribute to supervision activities where appropriate. Contribute to the effective operation of the Centre through participation in administrative activities, strategic initiatives
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: Probabilistic generative models (VLMs, diffusion, flow models) Reinforcement learning & Markov decision processes Causal inference & counterfactual reasoning Mechanistic & physics-informed modeling Agentic AI
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productisation of solutions, including experimental design and prototype validation. Lead and manage projects focused on product innovation, including, Artificial Intelligence, renewable energy, circular economy
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development programmes. The fellowship is tenable for one year. On an exception basis, a two-year programme may be supported. Service Commitment One year for every year of sponsorship. Application Process
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, with applications ranging from scientific research to medical imaging and marketing analysis. With the ever increasing amount of learning data, these algorithms face computational challenges