34 computer-programmer-"FEMTO-ST"-"FEMTO-ST" Fellowship positions at UiT The Arctic University of Norway in Norway
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Disrupted Planet ”. The objective of the position is to complete research training to the level of a doctoral degree. Admission to the PhD programme is a prerequisite for employment, and the programme period
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of the position is to complete research training to the level of a doctoral degree. Admission to the PhD programme is a prerequisite for employment, and the programme period starts on commencement of the position
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collaboration in Work Package 3, Professional Practices in Sápmi, at Arctic Youth. The objective of the position is to complete research training to the level of a doctoral degree. Admission to the PhD programme
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of the position is to complete research training to the level of a doctoral degree. Admission to the PhD programme is a prerequisite for employment, and the programme period starts on commencement of the position
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training to the level of a doctoral degree. Admission to the PhD programme is a prerequisite for employment, and the programme period starts on commencement of the position. The PhD fellow position is for a
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(MRI). The research will be of practical relevance to current societal challenges related to education and health. The C-LaBL Mentorship Program will provide a comprehensive training scheme to a
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aquaculture and fisheries. The position is for a period of four years. The nominal length of the PhD programme is three years. The fourth year is distributed as 25 % each year and will consist of teaching and
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perspectives. FemArc is a central activity in ConGender, focused on work in progress, and recruits beyond ConGender. The teaching consists of a one-year programme at BA level and two PhD courses. The one-year
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to the research group Harvest at the Norwegian College of Fishery Science (NCFS). The position is for a period of four years. The nominal length of the PhD programme is three years. The fourth year is distributed
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the group's research on developing novel machine learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image