44 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at UiT The Arctic University of Norway
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of Norway through the “Arctic MSCA-PF program”. Successful postdoc candidates will be invited for a two-day MSCA-PF symposium June 10-11, 2026 (hybrid physical/digital event, see details at https://uit.no
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through the “Arctic MSCA-PF program”. Successful postdoc candidates will be invited for a two-day MSCA-PF symposium June 10-11, 2026 (hybrid physical/digital event, see details at https://uit.no/arcticmsca
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event, see details at https://uit.no/arcticmsca/programstructure ). At this event, the candidates will present their past research achievements, discuss future plans with their potential supervisor and
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physical/digital event, see details at https://uit.no/arcticmsca/programstructure ). At this event, the candidates will present their past research achievements, discuss future plans with their potential
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practical information about working and living in Norway can be found here: https://uit.no/staffmobility Application Your application must include: Cover letter explaining your motivation and research
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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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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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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 how to write a
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hazards, enhancing asset protection, maritime security, emergency preparedness, and societal resilience. The project will leverage advanced AI and machine learning techniques to enable predictive risk