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Institute of Particle Astrophysics and Cosmology (BIPAC), on research aimed at extracting cosmological information from large-scale structure (LSS) and Cosmic Microwave Background (CMB) probes on very large
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://aria.org.uk/opportunity-spaces/resilient-climate-and-ecosystems/accelerated-adaptation for more information on ARIA and this programme). The project is a collaboration between groups at the Universities
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on probability, partial differential equations, large deviations, stochastic analysis, optimisation and machine learning. The successful candidate will contribute to the activities of the Machine Learning & Data
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Neurodevelopment. You will develop, implement, and apply computational pipelines to analyse large-scale genomic datasets generated through Perturb-seq and other functional genomics approaches. You will use state
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(Large Language Models, Convolutional Neural Networks, Machine Learning) for analysis and classification of data.
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, working closely with the Core Outcome Measures in Effectiveness Trials (COMET) Initiative. You will develop and evaluate natural language processing and machine-learning methods (including large language
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quantitative proteomic data with a degree of statistical rigour using common freely available software packages; proficiency in handling large datasets including bioinformatics and biostatistics; and knowledge
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B cell lymphomas including diffuse large B cell lymphoma and Burkitt lymphoma. They arise as a byproduct of somatic hypermutation, when the enzyme activation-induced deaminase (AID) generates DNA
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. Experience analysing large biological datasets, including RNA sequencing, metabolomics, proteomics or whole-genome sequencing data, is essential, along with strong quantitative and computational skills
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to develop new AI-assistive technology for detecting congenital heart conditions (CHDs) from fetal heart ultrasound scans. The Oxford team, in partnership with 5 hospitals sites, has curated a large fetal