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- University of Oslo
- University of Bergen
- UiT The Arctic University of Norway
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- NTNU Norwegian University of Science and Technology
- NTNU - Norwegian University of Science and Technology
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research in various areas of mobile network systems, multimedia and AR/VR/XR systems, robotics and machine learning, focusing on fundamental aspects as well as on applications in multidisciplinary contexts
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competence that meets the requirements for a position as associate professor in Norway, NTNU will arrange for you to acquire such competence during the employment period. In such cases, you will also be
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project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD fellow will be part of a growing
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on the development of machine learning algorithms, particularly transfer and adaptive learning, for multimodal wearable biosensing and its translation to rehabilitation and digital health applications. It is co
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with programming in Python is a requirement Experience with telecentric particle imagers, image analysis, and machine learning for particle recognition is an advantage Experience of working with wave
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, robotics and machine learning, focusing on fundamental aspects as well as on applications in multidisciplinary contexts. This position is part of the DRIVE project, funded by the Research Council of Norway
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of statistics and machine learning. Modern vessels produce vast amounts of multivariate data streams. The project addresses the development of trustworthy statistical and machine learning methods for anomaly
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of Oslo’s Department of Informatics (IFI) and is hosted by the Network and Distributed Systems Research Group (ND) with co-supervision from IFI’s Machine Learning section and the University of Inland
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of future reactor systems with a focus on systems relevant for Norway. The objective is to further develop and validate machine-learning surrogate models derived from high-fidelity multiphysics simulations
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Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including