201 parallel-and-distributed-computing-"U"-"Washington-University-in-St" positions at University of Cambridge in United Kingdom
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Researchers who are based across Africa and other locations in the UK, mainland Europe and Scandinavia. MAEASaM is an international, multi-partner programme. The University of Cambridge serves as the lead and
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The Mitchell Group at the Cambridge Early Cancer Institute is seeking a highly motivated and skilled Research Assistant with a background in computational biology to join their team specialising in
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of researchers who conduct cutting-edge research into NLP and AI within the University of Cambridge. Education: An excellent first degree in computer science, engineering or a closely related field Skills and
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subject who have the ability to lead an exciting, innovative and fundable research programme. The applicant would typically have at least 3 years of post-doctoral experience and may already have experience
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of the research programme is to develop EHR common data model specifications and to advance knowledge in the field of psychiatry EHR research, including clinical risk prediction modelling. The appointee will work
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atmospheric science, computational mathematics and physics. Experience with the Unified Model or atmospheric modelling is highly desirable but not essential. Experience with radiative transfer models
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Programme and other scientific studies. Develop automated methods and software to support large-scale bioinformatics analysis. Collaborate closely with a multidisciplinary team of clinicians, biologists, and
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programme including free yoga and bootcamp sessions To learn more about this role, or to make an informal enquiry, please contact the Head of Hospitality and Catering, Catherine Kearsey, on catherine.kearsey
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Strategic Center of Research (SCOR) at the Department of Haematology. The SCOR is led by Professor George Vassiliou and is conducting research to develop a clinical programme for myeloid cancer prevention
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is to design and develop analytical, computational, and mathematical methods to understand the fundamental processes that govern the evolution of antigenically variable viruses. Our research is highly