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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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, machine learning, generative AI and related fields as part of the DataSig II grant “High order mathematical and computational infrastructure for streamed data that enhance contemporary generative and large
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: Molecular modelling and simulation experience during PhD Ability to present complex information effectively to a range of audiences Experience of manipulating and visualizing large datasets We value
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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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, biomonitoring, and toxicity data. Key responsibilities: Lead UCAM's contribution to Task 5.2, including modelling of respiratory deposition of indoor particulate matter and estimation of exposure to aerosol-bound
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. The role includes implementing developed techniques and algorithms in high-quality software that adheres to group standards and evaluating them on large-scale benchmarks. You will take an active role in
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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