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the Faculty of Life Sciences & Medicine Hub for Applied Bioinformatics. This post is jointly funded by the Borne Foundation (50%) and King’s Health Partner’s Centre for Translational Medicine (CTM) (50
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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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-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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experience: Essential criteria PhD awarded in genetic epidemiology, statistical genetics, bioinformatics or related discipline Postdoctoral research experience in genetic epidemiology, statistical genetics
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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of the page. This document will provide information of what criteria will be assessed at each stage of the recruitment process. We pride ourselves on being inclusive and welcoming. We embrace diversity and want
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future development of an ongoing epilepsy research project funded by the UK Epilepsy Research Institute. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on
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information of what criteria will be assessed at each stage of the recruitment process. Further Information We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel
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page after you click “Apply Now”. This document will provide information of what criteria will be assessed at each stage of the recruitment process. In applying for this role please provide: A covering
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will be assessed at each stage of the recruitment process. We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel that they belong and are connected to others