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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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for clinical sample processing and multiomics approaches (including transcriptomics, genomics, metagenomics etc.). We have joined our teams to provide bespoke support and analysis of single cell, single nuclear
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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 that they belong and are connected
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criteria 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
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be found in the Job Description document, provided at the bottom of the page. This document will provide information of what criteria will be assessed at each stage of the recruitment process. We pride
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document will provide information of what criteria will be assessed at each stage of the recruitment process. We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how
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productivity & Ability to work under pressure and to meet deadlines Desirable criteria Experience of processing and analysis of human genomic, transcriptomic (including single-cell) and phenotype data, including
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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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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