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of Informatics, Uni-versity of Oslo, and will be part of a growing research agenda at the intersection of epidemiology, statistical modeling, machine learning and public health data systems. The project aligns
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) in causal inference (e.g., statistics, econometrics, epidemiology, bioscience, data science), or a relevant quantitative discipline. Essential Application Understanding of non-randomised studies and
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of epidemiology, statistical modeling, machine learning and public health data systems. The project aligns with recent developments at the HISP Centre at UiO, which is expanding its long-standing DHIS2
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women’s reproductive health, leveraging on biomedical science, digital medicine, translational clinical work, public health and epidemiology, and health communication and public outreach. Appointments will
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sciences, epidemiology, psychology, sociology). Evidence of a strong track record of research outputs in high-quality, peer-reviewed journals/outlets relevant to the discipline. Demonstrated impact and
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doctoral degree (e.g. Doctor of Medicine, PhD, or equivalent) in a relevant discipline, such as epidemiology. • Experience with systematic review and meta-analysis. • Quantitative data analysis skills
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and society. Integreat draws on the research strengths of researchers and students from the departments of Mathematics, Informatics, Philosophy, and the Oslo Centre for Biostatistics and Epidemiology
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may come from a diversity of disciplinary backgrounds, including but not limited to veterinary medicine, epidemiology, ecology, biology, computer/data science, economics and other social/behavioral
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disciplinary backgrounds, including but not limited to veterinary medicine, epidemiology, ecology, biology, computer/data science, economics and other social/behavioral sciences, nutritional sciences, etc
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, Informatics, Philosophy, and the Oslo Centre for Biostatistics and Epidemiology at UiO, the Norwegian Computing Centre (NR) and the ML group at UiT, with members from the departments of Physics and Technology