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the Medical Research Council. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of
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into the plasticity of pluripotent stem cells and their underlying gene regulatory network. Applicants should be motivated by fundamental scientific curiosity with a particular interest in stem cells and cell fate
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-relevant work in AI or a related field. Effective communication of complex scientific ideas to a non-technical audience. Application should include: A short CV. A summary, in one page or less, of your
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contract up to May 2026 Background We are seeking an ambitious Research Fellow to work on renewable energy systems and AI. You will join the Birmingham Energy Institute, a vibrant community of colleagues
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communities (Theme 4). This post provides an exciting opportunity to join our thriving research community and should be attractive to emergent leaders wanting to profit from an energised Women’s Health research
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supervision Proven ability to adapt to the changing requirements of the Higher Education community. Evidence of ability to participate in and develop both internal and external networks and utilise them
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An exciting opportunity has arisen for an ambitious researcher to join our team working on a UKRI-funded project named African SCENe (Sustainable Community Energy Networks). This project aims
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Collaborations: Build productive networks with researchers and clinicians locally, nationally, and internationally to enhance the Centre's reputation. Secure Funding and Disseminate Findings: Apply for research
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: Build productive networks with researchers and clinicians locally, nationally, and internationally to enhance the Centre's reputation. Secure Funding and Disseminate Findings: Apply for research grants
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Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of Electronic Health Records from people with epilepsy across multiple NHS