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aim to uncover the evolutionary processes contributing to the emergence and transmission of VOCs. Gaining insight into these mechanisms is crucial for informing the development of both improved and
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also provide support to bioinformatic analyses and data management. The successful candidate should have experience with analysing genomic data, be willing to work as part of a team and to be open-minded
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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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with community gatekeepers, stakeholders and groups Experience of project management – working in parallel on projects of different scale and complexity, prioritising and meeting deadlines within budget
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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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statistical genetics, bioinformatics, psychiatric epidemiology, data science, or a related field Strong programming skills (e.g. R, Python, bash) and experience in LINUX/UNIX environments Experience working
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are required in this context; the post holder is responsible for putting these in place. The post holder works closely with the biobank and bioinformatics teams to ensure efficient data processing, storage and
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transformation initiatives and driving sustainable improvements to our core business. Drawing on various process improvement methodologies, you will improve our efficiency by identifying and eliminating the root
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-on management and execution of the migration process, ensuring all objectives are met within the specified timeframe. The role holder will ensure that necessary functionality can be supported across existing
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Turnitin and Kaltura. They will also contribute to training delivery, process improvement, and the evaluation of digital education practices. The successful candidate will play a key role in fostering