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common complex human genetic diseases using genomics and other omics data within large-scale health datasets. The postholder will contribute to objectives and development of a research programme funded by
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common complex human genetic diseases using genomics and other omics data within large-scale health datasets. The postholder will contribute to the objectives and development of a research programme funded
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common complex human genetic diseases using genomics and other omics data within large-scale health datasets. The postholder will contribute to objectives and development of a research programme funded by
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models. These models will leverage both imaging data and corresponding radiology reports during training to build comprehensive representations that capture the rich, complementary information contained in
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common complex human genetic diseases using genomics and other omics data within large-scale health datasets. The postholder will contribute to the objectives and development of a research programme funded
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to automate scientific discovery in both the natural and social sciences. The postholder will contribute to one or more of the following strands: • Foundational work on large-scale/foundation models and
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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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Alonso and others at the Beecroft Institute of Particle Astrophysics and Cosmology (BIPAC), on research aimed at extracting cosmological information from large-scale structure (LSS) and Cosmic Microwave
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, BLIP), fine-tuning large language models for clinical NLP, and self-supervised contrastive learning—the models will learn to effectively combine visual and textual information. By developing
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of thousands of individuals, large-scale single-cell data from primary human tissues, spatial transcriptomic data, and experimental genomic screens. The postholder will join a multidisciplinary team based in