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ecosystem, democratising access to data, technologies, and AI expertise, while directly contributing to patient care and innovation. PharosAI offers more than a job—it offers a mission. You’ll be part of a
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to achieve a higher degree during the fellowship (e.g. PhD) and will need to have excellent academic and organizational skills, ideally with previous experience of data analysis and/or genetics. About the
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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
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Right to work: Applications from job seekers who require sponsorship to work in the UK are welcome and will be considered alongside all other applications. For further information visit the UK Visas
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, Professors Ruth Keogh and Kate Walker. Applicants should have a postgraduate degree, ideally a PhD, in medical statistics, epidemiology, health economics or a related field. Relevant experience in applying
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PhD) while conducting highly policy relevant research. Applicants should have a postgraduate degree with MRCP or MRCS. Relevant clinical experience in providing cancer treatments, co-ordinating clinical
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analytic codes to investigate the benefits and harms of medications. Candidates must have a doctoral degree (or be within 3 months of anticipated completion of a PhD) in medical statistics or epidemiology
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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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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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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