20 postdoctoral-image-processing-in-computer-science Fellowship positions at KINGS COLLEGE LONDON
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-quality teaching. The Hub for Applied Bioinformatics (HAB) is the Faculty’s focal point for computational biology, delivering bespoke bioinformatics support and training across genomics, transcriptomics
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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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Gibbons, working in close collaboration with Dr Lisa Story and the Perinatal Imaging team. The post will be based at the St Thomas’ Hospital campus and the postholder will be responsible for pregnant
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the supervision of Prof Amedeo Chiribiri within the Department of Cardiovascular Imaging, School of Biomedical Engineering & Imaging Sciences, King’s College London. About The Role Applicants should be medically
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the supervision of Prof Amedeo Chiribiri within the Department of Cardiovascular Imaging, School of Biomedical Engineering & Imaging Sciences, King’s College London. About The Role Applicants should be medically
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About us: This is an exciting opportunity to join the Health Psychology Section , part of the School of Mental Health & Psychological Sciences at King’s College London based at Institute
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About Us Applications are invited for the prestigious King’s College London Prize Fellowships. We want to recruit outstanding postdoctoral basic or translational scientists who are looking
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learning, computer science, physics, statistics, mathematics or related field. Demonstrated experience designing and developing novel machine learning and/or computer vision methods for either computational
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analysis of data, leading to peer reviewed papers and/or presentation of findings at conferences Evidence of excellent scientific analysis skills Strong computer skills and knowledge of computer programming
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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 Electronic Health Records from people