30 density-functional-theory-molecular-dynamics Fellowship positions at University of London
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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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Disability programme/project management. The ability to communicate and negotiate effectively at all levels is essential. Further particulars are included in the job description. The post is part-time 21 hours
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environmental epidemiology research team at LSHTM to work on the new HEARTH project: National hub on net zero, health and extreme heat, which is funded by UKRI and NIHR. This is an exciting opportunity to be part
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Group and will work closely with external collaborators. The post-holder must have a postgraduate degree, ideally a doctoral degree, in a relevant topic and experience in epidemiological research using
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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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environmental epidemiology research team at LSHTM to work on a new UKRI-funded study in the field of climate change and health entitled THERM-UK. This is an exciting opportunity to be part of a large
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in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice. The role involves leading research on the impact of community
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days per year, pro rata for part-time staff. In addition to this there are discretionary “Wellbeing Days”. Membership of the Pension Scheme is available. The post is based in London at LSHTM
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be subject to the LSHTM terms and conditions of service. Annual leave entitlement is 30 working days per year, pro rata for part time staff. In addition to this there are discretionary “Wellbeing Days
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to important public health topics. Studies will include descriptive epidemiology and use emulated target trial approaches for robust causal inference within large national health datasets. The post offers