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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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implementing novel data analysis and statistical methods for Type Ia supernova cosmology, and contributing to data acquisition and spectroscopic follow-up of upcoming large transient surveys (e.g. Rubin/ATLAS
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relativity, in particular the rigorous analytical study of cosmological big bang singularities and relationship to asymptotic notions of initial data. Start date: 01 February 2027 Fixed-term: The funds
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multilingual source material. You will have a strong understanding of deep learning and natural language processing, with practical experience of training, adapting and deploying large language models. You will
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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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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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Location: South Kensington (Hybrid) About the role: We are looking for a Postdoctoral Research Associate to join an Airbus- and Aerospace Technology Institute (ATI)-funded project to develop data
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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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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