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Field
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will have or be close to completion of a PhD/DPhil in Health Economics or related quantitative discipline, OR have a Master’s degree in Health Economics* or related quantitative discipline along with
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to other members of the group on health economic methodologies or procedures. About You You will have or be close to completion of a PhD/DPhil in Health Economics or related quantitative discipline, OR have
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but part time working would be considered (minimum of 4 days, 30 hours per week, 0.8 FTE About You To be considered for this position you should have a PhD degree (or be near completion) in a relevant
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to develop knowledge and understanding to form relationships for future research collaboration. To plan and manage own research activity, research resources in collaboration with others and contribute
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PhD (or be near completion) in a field such as AI, computer science, signal processing, biomedical engineering, have a strong record of publication in engineering or computer science literature, and
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of migration in cetaceans’. The post holder will provide guidance to less experienced members of the research group, including research assistants, technicians, and PhD and project students. The post holder will
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), to develop systems that improve the efficacy of machine learning-based technologies for healthcare applications. You must hold a PhD (or be near completion) in a field such as AI, computer science, signal
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for Doctoral Training in Soft Matter for Formulation and Industrial Innovation, www.dur.ac.uk/soft.matter/soficdt/about/, the Durham Centre for Soft Matter https://www.dur.ac.uk/soft.matter/, and the Biophysical
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for differential rate correction). The successful applicant will hold, or be close to completion of, a relevant PhD/DPhil in bioinformatics, comparative genomics and phylogenetics, together with relevant experience
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have completed, or be close to completing, a PhD/DPhil in a relevant quantitative field such as computational social science, computer science, or cognitive science. They will have a demonstrable track