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background in research in cardiopulmonary medicine or surgery and a PhD, although desirable, are not essential. We offer a generous reward package and benefits including: Competitive and attractive pension
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degree, ideally a PhD, in health economics, medical statistics, data science, epidemiology or a related field. A clear conceptual understanding of causal inference methods such as instrumental variable
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will have a PhD in a related field, an emerging track record of outstanding publications, and well-developed plans for new research projects. This post is generously funded by the A. G. Leventis
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postgraduate degree, ideally a PhD, in statistics, machine learning, or a related field. Experience of developing new statistical methods and a strong working knowledge of a statistical software package, such as
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independently and in close collaboration with in-country partners. The applicant should have an excellent academic track record that includes formal training in microbiology as well as a relevant PhD (public
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for cardiovascular disease in this patient group using linked electronic health record data. The post offers an excellent opportunity to develop expertise in risk prediction methodology for electronic health records
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relevant PhD and medical degree alongside registration with the GMC at Specialist Registrar Garde or below. You will have a recent track record in histopathology and use of machine learning techniques, and
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of the research project and develop new areas of research. The post-holder will be expected to undertake a higher degree such as a PhD during the fellowship. About You The applicant must be a medically qualified
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diseases, immune-modifying treatments and dementia risk using linked electronic health records. The post offers an excellent opportunity to design and conduct studies involving analysis of large-scale health
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PhD) while conducting highly policy relevant research. Applicants should have a postgraduate degree with MRCP or MRCS. Relevant clinical experience in providing cancer treatments, co-ordinating clinical