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About us: The Faculty: The Faculty of Natural, Mathematical & Engineering Sciences (NMES) comprises Chemistry, Engineering, Informatics, Mathematics, and Physics – all departments highly rated in
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, Statistics, Computer Science or conjugate subject and have a strong record of publication in the relevant literature. Good knowledge of machine learning algorithms is essential, as well as proven competence in
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of an extension, subject to funding. You will apply and develop cutting-edge machine learning methods to integrate and analyse multi-omic data to identify disease phenotypes. A key aspect of the role is to bridge
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-edge genome editing methods as part of the new MRC Centre of Research Excellence (CoRE) in Therapeutic Genomics. The main focus of the work is to develop a bespoke platform for using genome editing
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position to work with Professor James Davies and Professor Ronjon Chakraverty. This is an exciting opportunity to work on developing cutting-edge genome editing methods as part of the new MRC Centre
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software packages; proficiency in handling large datasets including bioinformatics and biostatistics; and knowledge of complementary structural and biophysical methods. Specialist knowledge in the discipline
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Translational Pathology Group and analyse bone biopsy samples using 2D and 3D tissue imaging and spatial transcriptomics technologies. They will develop and apply computational methods to integrate imaging
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collaboration with Prof Yulan He (KCL computer science) and her NLP group. The post is central to the NAVIGATE research programme, which investigates how different modes of AI uncertainty expression affect the
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interdisciplinary research programme focused on developing, evaluating, and scaling Nature-Based Solutions in Ethiopias drylands. The role will involve mixed-methods research, stakeholder engagement, participatory
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political psychology. The successful applicant will be supported in conducting quantitative research (e.g. multinational surveys, online experiments, analysis of existing data sets, computational textual