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for a further 6 months once in post. For more information: Prof Eric Robinson, eric.robinson@liv.ac.uk If you are still awaiting your PhD to be awarded you will be appointed at Grade 6, spine point 30
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research skills, provide instruction or plan/ deliver seminars relating to the research area. The successful candidates will have a PhD (or expect to soon be awarded) in the physical or biological sciences
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interest in infectious disease epidemiology are essential for the post. For appointment to Research Associate, you should hold a PhD in infectious disease epidemiology, evolutionary biology, bioinformatics
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inference attacks, to mitigate privacy leaks in MMFM. You will hold a PhD/DPhil (or be near completion) in a relevant discipline such as computer science, data science, statistics or mathematics; expertise in
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://degradationproject.com/ ) and NEXGENNa (http://nexgenna.org/ ) projects and participation in regular relevant FI meetings. Applicants should hold (or be about to obtain) a PhD in Chemistry, Materials Science, or a closely
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://degradationproject.com/ ) and NEXGENNa (http://nexgenna.org/ ) projects and participation in regular relevant FI meetings. Applicants should hold (or be about to obtain) a PhD in Chemistry, Materials Science, or a closely
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
, epidemiology, and socio-environmental modelling. To be considered a successful candidate; A PhD degree in Ecology, Biodiversity analyses, Environmental Science, Remote Sensing, Epidemiology, Data Science, or a
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be about to obtain) a PhD in chemical biology. They will be highly motivated and able to work independently. Excellent organisational and interpersonal skills are required to ensure success in liaising
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field such as psychology, mental health, behavioural science, digital health, or a related discipline Experience working with quantitative data and relevant software for data management and analysis (e.g
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have proven academic ability and a demonstrable high level of technical competence in computational data science and the analysis / modelling of the results. Theoretical or experimental experience of in