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ethos. You will be part of the Health Inequalities Policy Research (HIPR) Group, in the Department of Public Health, Policy and Systems at the University of Liverpool. The HIPR vision is to improve
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at the University of Liverpool, and will benefit from our adjacent Centre for Computational Biology. About you You will have a proven ability to develop innovative theoretical and/or computational methods. The most
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-based, policy-relevant epidemiological, interventional research and longer-term modelling. We seek highly motivated individuals with a PhD or equivalent in data science, applied statistics, geography
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the University of Liverpool, University of Southampton and National Oceanography Centre, in combination with project partners at Duke University, USA and University of California, Santa Cruz, USA. The
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statistical learning as it relates to biomedical applications and a desire to work collegially with researchers within the department and institute, as well as with clinical researchers to build an effective
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the University of Bristol, as well as Queen Mary University London, University of Birmingham, University of Cambridge University, and University of Liverpool. The aim of this collaboration is to uncover common
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researchers from the University of Bristol, as well as Queen Mary University London, University of Birmingham, University of Cambridge University, and University of Liverpool. The aim of this collaboration is
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independently once appropriate guidance has been provided. A PhD in biological science, computer science or statistics and/or relevant experience is required. This post is available immediately for 12 months in
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with a diverse training background to apply e.g. epidemiology, statistics, mathematics, engineering, physics, where you can apply your knowledge to health care data. You will have a PhD in epidemiology
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with a diverse training background to apply e.g. epidemiology, statistics, mathematics, engineering, physics, where you can apply your knowledge to health care data. You will have a PhD in epidemiology