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component disciplines; in explainable multi-modal deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in
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randomised controlled trial (RCT). This qualitative component will explore the experiences of individuals with Parkinson’s disease participating in a trial investigating the use of Functional Electrical
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also include planning for the dissemination and implementation of the tool. There will be opportunities to contribute to other elements of the research programme subject to capacity and competencies
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review and which potential new data should be prioritised for acquisition. To address this challenge we will advance the state-of-the-art across multiple component disciplines; in explainable multi-modal
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the required level of assurance. Please upload your CV and covering letter with your application. Where to apply Website https://www.timeshighereducation.com/unijobs/listing/406760/research-fellow-age
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the project forward and translate our research into tangible innovations for the UK’s growing photonics sector, building upon our recent work published in Nature (https://www.nature.com/articles/s41586-025
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emerging multi-omic measurements to build and test machine-learning-based multimodal risk prediction tools. A key element of the role will be to explore how different data types and AI-derived features can
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to help maintain and support employees’ well-being and work-life balance, please see our working with us webpages Where to apply Website https://www.timeshighereducation.com/unijobs/listing/406574/clinical
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component will explore the experiences of individuals with Parkinson’s disease participating in a trial investigating the use of Functional Electrical Stimulation (FES) to improve mobility. The findings from
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Connecting Generations aims to innovate in knowledge and understanding of intergenerational relations and population change. The Centre is led by Professor Jane Falkingham. For further details, see https