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of competitive benefits available to all staff for both work and personal life - https://hr.admin.ox.ac.uk/staff-benefits For informal enquiries about the post, please contact Ellie Tzima – ellie.tzima
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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projects within our group funded by EU Chips JU, Business Finland (BF) co-innovation, and Technology Programme Helsinki Institute of Physics such as Moore4Power, WIBASE, and AQUES respectively. The role
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and full GMC registration. What you will get in return: Fantastic market leading Pension scheme Excellent employee health and wellbeing services including an Employee Assistance Programme Exceptional
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
models. You will also have opportunities to contribute to open-source computational tools and datasets, teach master-level courses, and advise doctoral students. You will closely collaborate with Prof
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: The successful candidate will join the Distributed AI (DAI) group in the Department of Informatics, King’s College London. They will carry out research in neuro-symbolic AI, with a focus on using generative and
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writing skills are essential. Additional information: Interviews are planned for 17th March 2026. For further information and details of how to apply, please visit our website http://jobs.jic.ac.uk
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skills organised by the College of Medical, Veterinary and Life Sciences Graduate School. Full details of the programme are available at http://www.fom.gla.ac.uk/RDTC/ Regular attendance and contributions
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dynamic group investigating mitochondrial genetics and function using Drosophila as the model (http://themalab.co.uk ). The candidate will use genetic and biochemical approaches to study mitochondrial DNA
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of Salivary Cortisone Test (Re-SCAIT) study (funded by NIHR Health & Care Services Delivery Research (HSDR) https://fundingawards.nihr.ac.uk/award/NIHR174395 ) will evaluate the implementation and uptake