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, microbiology, epidemiology, modelling, and technology. This is achieved through a strong academic environment of international top class with correspondingly skilled researchers and employees. The Institute
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the Research Group for Genomic Epidemiology. By joining us, you'll gain expertise in AI, bacterial sequence analysis and food safety evaluation while contributing to one of the most challenges in food safety
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epidemiology or a related discipline and to have a track-record within the field of pharmacoepidemiology (in its broadest sense). Further, the candidate should contribute to a positive and constructive working
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Proven experience with epidemiological methods and data analysis Interest in applying AI tools to support scientific workflows A strong analytical mindset and attention to detail A systematic and
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, Statistics or other fields related to Epidemiology or Environmental Medicine. The ideal candidate has prior experience in working with health data, has previously worked with R or similar statistical analysis
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biosolutions. The institute’s tasks are carried out in interdisciplinary collaboration within e.g. nutrition, chemistry, toxicology, microbiology, epidemiology, modelling, and technology. This is achieved
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and documented experience from any or all the following areas will be prioritized: clinical and epidemiological research relating to back and neck pain or musculoskeletal health strong methodological
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Experience with digital health interventions Strong profile within research methodology (i.e. systematic reviews, epidemiology etc.) Experience and success with external funding (e.g. as co-applicant
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biosolutions. The institute’s tasks are carried out in interdisciplinary collaboration within e.g. nutrition, chemistry, toxicology, microbiology, epidemiology, modelling, and technology. This is achieved
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of the successful candidate should therefore be in the cross-field of data science, pharmacology, and clinical epidemiology. The candidate will be expected to establish a research team focusing on pharmaceutical data