30 phd-position-in-image-processing-"Prof" Fellowship positions at University of London
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postgraduate diploma in Veterinary. A 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
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, conducting simulation studies, analysis of datasets from economic and social research studies, software implementation and delivery of workshops. The Research Fellow will be supervised by Prof. Jonathan
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is Prof Cally Tann. We are seeking a Research Fellow in Early Child Development & Disability to coordinate the development, implementation and evaluation of the programme, including: mixed methods
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Research Fellow. The successful candidate will be a medical doctor with experience of providing cancer treatments (radiotherapy or surgery), and will join a new NIHR-funded research project called TACTIC
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for an enthusiastic and highly motivated Research Fellow to join the world-leading tuberculosis (TB) Modelling group at LSHTM. The successful candidate will be supervised by Dr Rebecca Clark and Prof Richard White and
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-led by Queen Mary University of London. PharosAI is set to revolutionise AI-powered cancer care, accelerating the development of breakthrough therapies, advancing clinical applications, and improving
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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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fellow position within the William Harvey Research Institute at Bart’s and The London Medical School, Queen Mary University of London (QMUL). The post-holder will work on projects including the PinG study
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Commission (ITC); offering an early career EHR scientist a unique opportunity to develop a transnational research portfolio. We wish to appoint to a full-time position in the Department of Non-Communicable
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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