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-machine interfaces. Experience with AI methods such as generative models, reinforcement learning, online/adaptive learning, or uncertainty quantification. Research experience in rehabilitation engineering
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. The objective is to further develop and validate machine-learning surrogate models derived from high-fidelity multiphysics simulations of reactor transients and quantify how surrogate uncertainties propagate
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and oral). Applicants must be well-trained in conducting quantitative analyses. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage
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and Distributed Systems Research Group (ND) with co-supervision from IFI’s Machine Learning section and the University of Inland Norway’s research group for User Perception and Engagement in XR
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or game theoretic analysis. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. The evaluation of applicants
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machine learning is an advantage but is not required. Experience with the design and implementation of survey-experiments is an advantage but not a requirement. Alongside developing their own research ideas
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, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. The evaluation of applicants primarily hinges on their documented academic qualifications and the