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for admission to the faculty's PhD program in Computer Science , see Section 6-1 of the PhD regulations for more information. You must have a relevant Master's degree in information systems or interdisciplinary
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years. About the PhD training: As a PhD research fellow, you will take part in the PhD programme at the Faculty of Social Sciences, UiB. The programme corresponds to a period of three years and leads
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to the faculty's Doctoral Programme (link), see Section 6-1 of the PhD regulations for more information. You must have a relevant Master's degree in Computer Science, Cyber Security, or an equivalent field. Your
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to the Faculty's Doctoral Programme . You must have English language skills, both written and spoken, corresponding to the scale B2 in the Common European Framework of Reference for Languages (CEFR). Applicants who
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that can be used to implement the project Mentor programme as a new employee at NTNU Favorable terms as a member of the Norwegian Public Service Pension Fund (SPK) Free Norwegian language training at a
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for admission to the faculty's Doctoral Programme (link), see Section 6-1 of the PhD regulations for more information. Fluency in Norwegian or another Scandinavian language is required, as well as good oral and
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for admission to the faculty's Doctoral Programme , see Section 6-1 of the PhD regulations for more information. You must have a relevant Master's degree in Computer Science or equivalent. Your degree must
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education programme Carry out high quality research Develop material integrity predictive framework using different experimental and simulation approaches Participate in project meetings with supervisors and
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particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme. Good oral and written presentation skills in English. You need strong skills in
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meet the requirements for admission to the faculty's doctoral programme in Engineering Cybernetics . Strong programming skills, in particular Python, and practical experience with modern machine learning