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We are seeking a brilliant post-doctoral researcher to lead on a range of innovative epi-/genetics and neuroimaging projects related to an ERC/UKRI-funded 5-year project on BrainHealth . The post is available as either a Grade 7 Research Associate or Grade 8 Research Fellow post, dependent on...
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, environmental economics, green finance, ethical supply chains, and behavioural change. Digital and Technologies: AI and machine learning, cybersecurity, spatial intelligence, robotics, human-computer interaction
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Research Fellow to lead a programme of quantitative behavioural research to advance understanding of climate-relevant behaviour change and life choices. The role will involve co-designing research with
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funded by the NIHR Health and Social Care Delivery Research (HSDR) Programme for five years. SURGE is led by senior researchers at UWE Bristol and brings together co-applicants from 13 partner
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Machine Learning Research Group within the Department of Computer Science. Further information This post is a full time, fixed term contract for two years, with a potential extension to three years
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an engaging experience and a supportive programme. About you Our ideal candidate will hold a PhD or equivalent in a relevant discipline, along with a UG degree or equivalent experience. They will be a
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an international reputation for teaching and research that develops theory, policy and practice in relation to education internationally. We offer one undergraduate programme (Education with Psychology) as well as a
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experience of working with colleagues in a professional working environment. Eligibility The programme is open to most University of Bath undergraduate students in years one and two. If you’re studying a
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expected to be on organisation design projects within the Momentum, Capacity and Capability Programme, though your portfolio may shift as institutional priorities develop. In this role, you will: Lead
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of this project is to test, calibrate and quantify biases in spectroscopic inferences, particularly around galactic outflows. Extensive computational resources on STFC-DiRAC and connections to observational