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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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research networks such as the Centre of Data and Statistical Science for Health (DASH) (https://www.lshtm.ac.uk/dash ) and the Multi-City Multi-Country Collaborative Research Network (https
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in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice. We are seeking a researcher with skills in quantitative data
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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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Salvador, Brazil. The post-holder will also contribute to the laboratory analysis, data cleaning and management, and data analysis and write-up a study to assess environmental exposures to enteric pathogen
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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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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
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Infectious Disease Epidemiology & Dynamics department at LSHTM to work on polio eradication. This role utilises global surveillance data for polio to inform understanding of the status of eradication and
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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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for a Research Fellow in Bioinformatics/Computational Biology to help develop, coordinate, and conduct robust analysis of high-throughput host protein data under supervision using advanced analytical and