603 computer-science-intern "https:" "https:" "https:" "https:" "Central European University" Postdoctoral research jobs in United Kingdom
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international networks, close links to museums and heritage organisations, and an active programme of seminars, workshops, and public events. The post will also require significant amount of time spent
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with Dr. Li and in collaboration with national and international interdisciplinary partners based at the UK Health Security Agency (UKHSA), University of Cambridge, Imperial College London, and John
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About the role This role supports a multidisciplinary programme investigating the role of the Maresins, a family of pro-resolving lipid mediators, in early cancer development. You will process
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qualitative work. About You Candidates should hold a PhD (or be close to completion) in a relevant discipline, such as epidemiology, social epidemiology, data science, or medical statistics. We are looking
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About the role We have an exciting opportunity to join Dr. Dhanu Gupta’s Research Group in the Department of Paediatrics as our Postdoctoral Researcher in Nanomedicine and Synthetic Biology. You
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: Essential criteria PhD in applied mathematics, statistics, engineering, computational biology, econometrics, or a related discipline. Experience in developing complex models using real-world data, with strong
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to the Department of Biology , and will be line managed by Lindsey Gillson (Biology) with mentorship from Guy Schofield (Archaeology), Marian Ursu (Arts & Creative Technologies), Christopher Lyon (Environment
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list of organisations and social movements through desk‑based mapping and collaboration with international partners, including NGOs, advocacy groups, universities and CLAN network members. Collect
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-tier conferences and journals in AI as well as, ideally, AI and Law. A strong computer science background with a focus on AI with experience, including a proven publication track-record, in at least two
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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials