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multiple imaging modalities—initially concentrating on whole-body and abdominal MRI—using UK Biobank imaging data. About the Role The post is funded for 3 years and is based in the Big Data Institute, Old
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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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, 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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. About the Role The post is funded for 3 years and is based in the Big Data Institute, Old Road Campus. You will join an interdisciplinary team of researchers spanning imaging science, machine learning
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foundational theory of how large ML systems can be regularised to have dramatically fewer trainable parameters without sacrificing accuracy by analysing the use of low-dimensional building blocks Implicit
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You will have or be close to the completion of a PhD/DPhil in epidemiology, biostatistics or big data, along with demonstrable experience of working with population registers and large datasets. With
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between the two linked studies as well as taking the lead in the large-scale qualitative secondary analysis of interview data from multiple sources. In this role you will be expected to contribute
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equivalent to PhD level in health data science or similar field. Experience working within multidisciplinary teams, in medical statistics and in the analysis of large healthcare datasets (such as CPRD
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be close to the completion of a PhD/DPhil in epidemiology, biostatistics or big data, along with demonstrable experience of working with population registers and large datasets. With proven
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language processing, historical linguistics, and computational humanities. The postholder will lead the computational stream of the project, which involves building a large corpus of Latin texts (data collection and