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. Familiarity with statistical analysis software (e.g., STATA, R, SPSS) or computer programming (e.g. C++, Python, R) and experience working with health-related data will be advantageous. The ability to work
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the appropriate area. Familiarity with statistical analysis software (e.g., STATA, R, SPSS) or computer programming (e.g. C++, Python, R) and experience working with health-related data will be advantageous
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partners. Main Duties Improve, develop, implement, and apply advanced computational tools and workflows to process, analyse, and interpret large-scale LCMS-based metabolomics datasets across multiple species
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and show strong quantitative skills, proficiency in coding (e.g. Python or MATLAB), and experience handling large datasets. Knowledge in at least one of the following is essential: ocean interior
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partners. Main Duties Improve, develop, implement, and apply advanced computational tools and workflows to process, analyse, and interpret large-scale LCMS-based metabolomics datasets across multiple species
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end users Proficient in programming or scripting languages, including Python, C++ and Java Script Ability to work independently on multiple streams of research and collaboratively across multi
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Electronic Health Records from people with epilepsy across multiple NHS hospitals in England and Wales. They are expected to have some experience working with NLP in general and LLMs in particular. Familiarity
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quantitative techniques (e.g., NLP, classification, clustering), statistical modelling, and other computational techniques Process large scale text data sets in multiple languages Create documentation for data
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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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Electronic Health Records from people with epilepsy across multiple NHS hospitals in England and Wales. They are expected to have some experience working with NLP in general and LLMs in particular. Familiarity