147 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions in Norway
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to complete the final exam. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome
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% teaching component. The Department teaches in all the sub-fields mentioned above. The successful candidate will be part of the Faculty’s PhD programme. The work is expected to lead to a PhD in political
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Econometrics Virtual power plants Power systems and/or power electronics Machine learning Renewable energy systems Advanced statistics Language requirement: Good oral and written communication skills in English
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candidates will be admitted to the PhD program in Health and Medicine. The education includes relevant courses amounting to about six months of study, a dissertation based on independent research
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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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candidate will be part of the Faculty’s PhD programme. The work is expected to lead to a PhD in political science. Required qualifications Formal qualifications Education equivalent to five years
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three years with research duties exclusively. The hired candidate will be admitted to the PhD program in Social Sciences. The education includes relevant courses amounting to about six months of study, a
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an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural
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an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural
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must participate in an approved educational programme for a PhD degree within a period of 4 years. The deadline for applying for admission to the PhD programme at The Faculty of Science and Technology is