594 parallel-processing-bioinformatics-"DIFFER" Fellowship positions in United Kingdom
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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
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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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individually, make a real difference. The role The Institute for Communication Systems (ICS), home of the 6G Innovation Centre (6GIC) is looking to recruit a full-time post-doctoral Research Fellow to work
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, Italy and the UK. We will use artificial intelligence, bioinformatics, next generation sequencing and microbiology. The successful candidate will work closely with an interdisciplinary team of academics
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ourselves into one of the best universities in the world. Our culture empowers people to achieve this aim and to collectively, and individually, make a real difference. The role This is an exciting new
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ourselves into one of the best universities in the world. Our culture empowers people to achieve this aim and to collectively, and individually, make a real difference. The role This is an exciting new
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ourselves into one of the best universities in the world. Our culture empowers people to achieve this aim and to collectively, and individually, make a real difference. The role This is an exciting new
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to work with modern massively-parallel simulation codes. Candidates must have (or be close to completion of) a PhD in astrophysics or a related subject, and a BSc/MPhys (or equivalent) degree in physics
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scientific boundaries and overcome hurdles. They will have experience in stem cell culture, imaging, molecular biology, genetic engineering and/or bioinformatics analysis. This will enable new approaches
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at the Institute for Regeneration and Repair. In this role, you will contribute to cutting-edge research using genomic data to understand why critically ill patients respond differently to infection. Your work will