31 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at University of London
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to refereed journals, and work with and help supervise BSc, MSci, and PhD students. The successful applicant will have a PhD in Astrophysics, Theoretical Physics, or a related discipline and prior experience
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You The successful candidate will have a PhD (or soon to be awarded), or equivalent experience which has involved significant practical cell culture, and ideally experience with molecular biology
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are seeking highly-motivated researchers holding or near completion of a PhD in a relevant biological or life sciences subject. The ability to develop independent insights into the project, solve complex
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Research Council’s (AHRC) Bridging Responsibilities AI Divides (BRAID) programme that will explore new technologies, new business models and new approaches to data provenance in pursuit of an equitable
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stakeholders. The post holder will be expected to undertake interviews and focus groups, and support the quantitate analysis for this project. About You You will be a highly motivated individual with a PhD (or
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in the ongoing drive to reduce animal use in scientific research. Applicants must hold a PhD in Cell Biology or related discipline and have a track-record of success, as indicated by first-author
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should have a PhD (or close to completion) in applied health-related social science (e.g. health psychology, medical anthropology, public health). Applicants require experience of running research studies
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complex study. Applicants must have a PhD in a relevant subject. The study requires substantial skills in cell and molecular biology and will require vivo testing of the newly created cell-models. Omics
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About the Role To undertake research investigations in collaboration with and under the supervision of Dr Saroash Shahid and Prof Mangala Patel, to deliver the objectives of the research programme
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at the Barts Cancer Institute (Queen Mary University of London). This role will involve analysing existing spatial-omics data sets and developing novel computational tools to understand the risk of developing