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Field
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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 the project. Applicants should hold, or be close to completion of, PhD/DPhil in Biology or a related subject. You should have a high level of competence in cell biology and relevant experience demonstrated by
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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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other partners in the project. About you You should possess a PhD/DPhil in Engineering, Computer Science or other related field, (with the possibility to underfill at Grade 6 (£34,982 - £40,855 p.a
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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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possess a PhD/DPhil in Engineering, Computer Science or other related field, (with the possibility to underfill at Grade 6 (£34,982 - £40,855 p.a.) if candidate holds a relevant degree and is working on PhD
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protocols. You will work with limited supervision to design and accurately execute experiments to achieve the goals of the project. Applicants should hold, or be close to completion of, PhD/DPhil in Biology
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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
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computer codes to solve some of the daily research problems and have experience with high performance computing. You should have a PhD in Chemistry, Physics or Materials Science with a proven research track