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Field
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to participate in (courses from) the Master’s program in Epidemiology at Utrecht University. The interviews will take place in the week of 12 January 2026. Questions regarding this vacancy may be sent by e-mail to
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methodologies compatible with epidemiological principles to produce high-quality evidence and tools. In this inter-disciplinary PhD project, you will collaborate with other researchers from the NCSR in the above
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environmental epidemiology within an interdisciplinary research project investigating how early-life environmental exposures affect health and development in children and adolescents. The work is largely
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Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS GmbH | Bremen, Bremen | Germany | about 2 months ago
29 Oct 2025 Job Information Organisation/Company Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS GmbH Department Clinical Epidemiology Research Field Medical sciences » Cancer
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At the Leibniz Institute for Prevention Research and Epidemiology – BIPS GmbH there is an opening in the department of Clinical Epidemiology Epidemiologist (PhD candidate) (m/f/d/x) Salary Scale 13
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About the role Applications are invited for a Research Assistant/Research Associate in Viral Genomics and Immunology at the Department of Infectious Disease Epidemiology/ School of Public Health
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-MEDIC (ERC Starting Grant) Host: Institute of Clinical Epidemiology, Public Health, Health Economics, Medical Statistics and Informatics (EPICENTER), Medical University of Innsbruck Supervisor: Patrick
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support population-level prevention strategies. Further information: Applicants should have either a minimum 2.1 undergraduate degree in a relevant area (public health, epidemiology, economics, statistics
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, epidemiology, etc. or economics. The position is open to Australian and New Zealand citizens or Permanent Residents only. We are looking for students of high scholarly calibre with: A First-Class Honours degree
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these barriers by putting together a world-leading data resource on suicide and self-harm, and powerful machine learning methodologies compatible with epidemiological principles to produce high-quality evidence