19 parallel-computing-numerical-methods-"Simons-Foundation" Fellowship positions at University of London
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empirical research. They will oversee specific research tasks, develop new techniques, and generate original contributions to the programme while fostering a collaborative team environment. Key
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Applications are invited for this PhD training programme to commence in September 2026. Led by the London School of Hygiene & Tropical Medicine, this PhD Programme is offered by five UK and six
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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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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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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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project developing Bayesian causal inference methods for mediation analysis using Electronic Health Records (EHR) data. The Research Fellow will design and implement Bayesian methods and software
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, and OpenSAFELY, and to develop and apply the new generation of analytical methods to study environmental health risks and climate change. The post will provide opportunities for interactions with
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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
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data from the UK using robust causal inference methods. Based in London at the London School of Hygiene & Tropical Medicine, the post-holder will be embedded within the Electronic Health Records Research
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science methods, ideally to environmental health data, and conducting systematic reviews and meta-analysis. Further particulars are included in the job description. The post is full-time 35 hours per week