199 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions in Norway
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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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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invites applicants for four PhD Fellowships in subsurface characterization within geosciences, reservoir engineering, molecular modelling, and machine learning at the Faculty of Science and Technology
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the University’s/ Faculty’s/ Department’s approved PhD program and is expected to complete the project within the set fellowship period. The main purpose of the fellowship is research training leading
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NHM The research fellow must take part in the University’s/ Faculty’s/ Department’s approved PhD program and is expected to complete the project within the set fellowship period. The main purpose
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. The employment period will be three years of full time with no teaching obligations, and there is a premise for employment that the PhD Research Fellow is enrolled in USN’s PhD program in technology within three
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the Department). The position is funded by the University of Bergen. The PhD-project will be connected to the research group of Prof. Nathalie Reuter at the Department of Chemistry and the Computational Biology
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of Bergen. The PhD-project will be connected to the research group of Prof. Nathalie Reuter at the Department of Chemistry and the Computational Biology Unit. The position is affiliated to theproject Life
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of the following areas is an advantage: Modelling and simulations of flow in porous media. Programming, image processing and machine learning Personal and relational qualities will be emphasized. Motivation
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written and oral English. Experience from one or several of the following areas is an advantage: Programming, image processing and machine learning. Magnetic Resonance Imaging. Laboratory experience from