Research Fellow



    Ref: 038971
    We are seeking a highly motivated postdoctoral research fellow to develop a predictive quantitative framework for assessing the short- and long-term effectiveness of vaccination programmes in domestic livestock. The successful candidate will be responsible for developing mathematical models that combine field and experimental data in order to predict potential evolutionary and epidemiological consequences of vaccination in farm animals.
    The post is available at The Roslin Institute of the University of Edinburgh in Scotland, under the supervision of Drs. Andrea Doeschl-Wilson and Samantha Lycett. The Roslin Institute is part of the University of Edinburgh’s College of Medicine and Veterinary Medicine, and is internationally renowned for its multi-disciplinary research in animal health. The advertised position is part of the European Horizon2020 project SAPHIR: Strengthening Animal Production and Health through the Immune Response http://www.h2020-saphir.eu/. The successful candidate will thus interact with other group members and external collaborators from a variety of backgrounds, such as computational statistics, quantitative and molecular genetics, immunology and animal or veterinary science, and will be expected to present the work to an international audience.
    The post-holder must have a PhD (or near completion) in a scientific discipline with a substantial quantitative component (e.g. mathematics, statistics, theoretical or computational biology, or physics), together with evidence of skills in mathematical modelling and writing scientific publications. Extensive knowledge in R or other programming languages is required. The ideal candidate will have experience in modelling biological or ecological processes and a strong interest in infectious disease dynamics, be autonomous, organised and collaborative and also possess strong problem solving skills.
    The position is full-time and available for a minimum of 16 months, with an anticipated starting date of August 2017.


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