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for flexibility in the start and end dates, subject to approval by the department and the funding agencies. The successful candidate will join the Machine Learning & Data Science research group and conduct research
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Patient Advisory Groups (PAGs), conducting qualitative interviews, running focus groups, designing and executing large-scale international surveys with quantitative data analysis, and collaborating
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, Rubin-LSST, PFS, JCMT, Litebird as well as participation at co-I level of instruments on large explorer mission concepts such as Cassini and JUICE with participation in many other mission and instrument
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and spatial analysis techniques to large-scale spatial transcriptomics and imaging datasets, using tools such as MuSpAn to identify spatial biomarkers and uncover the biological mechanisms driving
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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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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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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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(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