21 parallel-computing-numerical-methods-"Prof" Fellowship research jobs at University of London
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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to join the Environment & Health Modelling (EHM) Lab (https://www.lshtm.ac.uk/ehm-lab ) led by Prof Antonio Gasparrini. The successful candidate will work on the project CONNECT – Cohort and environmental
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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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the College’s small animal referral hospital by further developing and delivering advanced cardiac surgical therapies through the open heart surgery programme, at the Royal Veterinary College. We are looking
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to improve people's health in developing countries by striving for excellence in research, healthcare, and training. Our research program spans basic scientific research, clinical studies, epidemiological
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, while engaging with researchers from different disciplinary and country backgrounds. For more information please contact: Prof. Dina Balabanova, dina.balabanova@lshtm.ac.uk . The post is part-time 17.5
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to cancer treatment delays. The successful candidate will join 50 researchers on 10 National Cancer Audits https://www.natcan.org.uk/ . The postholder will report to Prof Ajay Aggarwal (co-PI, TACTIC and
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demonstrable experience in analysing datasets such as infectious disease surveillance, applying statistical methods, and interpreting output. Further particulars are included in the job description. The post is
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to the set-up and conduct of a funded research project aiming to co-create a national weight management programme in Thailand. The duties of the post will involve coordinating and writing ethical approval
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research project on cardiovascular risk prediction for people with immune-mediated inflammatory disease. The successful candidate will use advanced risk prediction methods to develop prediction models