584 machine-learning "https:" "https:" "https:" "https:" "https:" "The University of Edinburgh" positions in Norway
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the areas of stochastic analysis and computational methods towards machine learning with focus on risk-sensitive decision making and control. Techniques may include forward, backward stochastic differential
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Interaction (HCI). The position will collaborate closely with (1) the NTNU Shore Control Lab (SCL), a design and human factors research environment focusing on human-centered design of technology for safety
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Africa and Europe. The network will consist of 15 PhDs, that together will work and learn as an international cohort and collaborate across projects to bring together insights anchored in different sectors
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. The applicant must have an academically relevant education corresponding to a five-year master’s degree or a cand.med.vet. degree, with a learning outcome corresponding to the descriptions in the Norwegian
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future plans with their potential supervisor and learn how to write a successful MSCA-PF application. The selected candidates will, jointly with the supervisor, write the MSCA-PF application by
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, see details at uit.no/project/arcticmsca/program-structure ). At this event, the candidates will present their past research achievements, discuss future plans with their potential supervisor and learn
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Energy and Environmental Economics, focusing on macroeconomic analysis of energy transition policies
in Norway. About the project This PhD position is part of the newly funded VISTA Centre for Norwegian Transformative Energy Policy Development (NOTRAP) (https://www.ntnu.edu/notrap ), which will
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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 necessary have a more critical look at how HPC
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learn how to write a successful MSCA-PF application. The selected candidates will, jointly with the supervisor, write the MSCA-PF application by the deadline of September 9, 2026. In this call we search
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the researchers from Department of Automation and Process Engineering will play a key role. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early