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. About the role As Postdoctoral Researcher in Big Data for Cardiovascular Population Health, you will play a key role in applying machine learning, health statistics, and large-scale medical and population
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for streamed data that enhance contemporary generative and large language models”. They will be expected to conduct research which falls within the remit of this large-scale project and will have the opportunity
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of research investigating cancer risk and prevention. The appointee will work closely with Professor Ruth Travis, Dr Karl Smith-Byrne and other members of the research team, using large-scale epidemiological
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public health. In this role, you will develop and evaluate novel AI and machine learning methods using large-scale multimodal datasets, contributing to epidemiology-informed foundation models, predictive
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-associated virus (AAV)-based gene therapy treatments for inherited retinal disease (IRD). The successful candidate will generate translational research data required to support the preclinical development
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protocols, and test hypotheses and analyse scientific data. You will be expected to contribute ideas for new research projects, develop ideas for generating research income, present detailed research
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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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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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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