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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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increasing importance due to the widespread use of AI and in particular machine learning (ML). As today’s mainstream AI/ML workloads often resort to large-scale and energy-hungry supercomputers, it is
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effectively exploited, possibly using some kind of machine learning algorithm, provides more accurate data than traditional data collection methods, e.g. paper-based surveys. This data is valuable to several
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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
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simulations combined with physics-informed machine learning will also be examined. Several research and industrial partners are a part of this project. The ideal candidate would combine strong computational
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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