506 parallel-processing-bioinformatics-"Multiple" Fellowship research jobs in United Kingdom
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to fostering a barrier-free and inclusive recruitment process. We are dedicated to providing reasonable adjustments for candidates throughout the application and interview stages, including accessible document
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help you succeed, we published Candidate Guidance to provide support on the application and interview process. Discover our benefits, visit Your Benefits website. We welcome applications from UK
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to provide support on the application and interview process. Discover our benefits, visit Your Benefits website. We welcome applications from UK, Europe and worldwide and aim to make your move to the UK as
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for a Research Fellow in Bioinformatics/Computational Biology to help develop, coordinate, and conduct robust analysis of high-throughput host protein data under supervision using advanced analytical and
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for a Research Fellow in Bioinformatics/Computational Biology to help develop, coordinate, and conduct robust analysis of high-throughput host protein data under supervision using advanced analytical and
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(where possible) in BioModels and other repositories Generation of synthetic data to represent rare disease patients that can be shared You have A PhD in bioinformatics, physics, or a related data
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months, in the first instance. Application Process If you would like to discuss this role please contact Prof Regent Lee at regent.lee@nds.ox.ac.uk Applications for this vacancy are to be made online. You
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Sciences sector here in NI, mapping and collaborating with other clusters in UK and Ireland. The post holder will develop bioinformatics tools, workflows, and cloud-based infrastructure to support genomics
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to contribute to an exciting European project developing hydrothermal processes for the management of solid digestate and other waste from the olive oil production process. About the Role We are seeking a
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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