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to CSRankings. Further information about research at the Department is available here: https://di.ku.dk/english/research/ . SODAS is an interdisciplinary social data science center at the Faculty of Social
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cohort studies. The position is for 3 years, starting on October 1, 2026, or as soon as possible hereafter. Information on the Department of Public Health can be found at https://publichealth.ku.dk/ Our
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candidate from their current and previous employers. Living and working in Denmark Foreign applicants will be offered Danish language training as part of the employment. The International Staff Office (ISO
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understanding of AI enabled education and be comfortable coordinating activities involving both pedagogical and technical stakeholders. Familiarity with large language models, Retrieval Augmented Generation (RAG
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research group or center you would like to join (max. 1,000 words). A current curriculum vitae (e.g. using the EU model), clearly stating your educational background, experience, techniques, language skills
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general. And more recently, the proposal of Large Language Models opened a wider range of opportunities to explore its use for Software Engineering (LLM4SE). This is the main goal of this research, i.e
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of this job advertisement. Copies of the publications marked with an *. Only publications written in English (or another specified principal language, according to research tradition) or one of the Scandinavian
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an *. Only publications written in English (or another specified principal language, according to research tradition) or one of the Scandinavian languages will be taken into consideration) A brief (1-page
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close to 50-50% gender balance. Our working language is English. We are highly interdisciplinary and include experts within biomedical engineering, neuropsychology, pharmacology, biophysics, medicine with
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within a research team. Danish language proficiency is an advantage but not a prerequisite. Experience with one or more of the following will be considered an advantage: dyadic data analysis; multilevel