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Sciences at UiT The Arctic University of Norway represents a newly created entity gathering almost all possible health study programs. This facilitates an unique interdisciplinarity and innovation in health
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communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic
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the subject Analyze data, assess different perspectives and draw well-founded conclusions Be flexible and open to adjusting the plan for the project as needed Emphasis will be placed on personal
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interdisciplinarity and innovation in health education and research. We work closely with the services in the North to solve tomorrow's challenges. Read more about us at uit.no/helsefak Apply for this job Deadline 17th
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the position. Project outline containing proposals for an overall description of research questions, theoretical perspectives, methodological design for the project and progress plan (maximum 1500 words/4 pages
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of welfare offers including voluntary language courses in Norwegian membership of the Norwegian Public Service Pension Fund The position is remunerated according to the State Salary Scale, salary plan
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PhD Research Fellow in Chemistry-focus on Experiments in Chemical Kinetics and Heterogeneous Catalys
be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment
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is a new initiative that organises the local bioinformatics community and drives bioinformatics innovation by integrating computational and biological sciences to address complex life science
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among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment of their class
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. The plan is for the candidate tofocus on Bayesian modelling in close collabora-tion with researchers at the HISP centre that work on complementary deep learning approaches to disease modelling as