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experimental approaches to develop and validate novel in vitro and ex vivo approaches that model arterial medial calcification without using any animal products. This work will represent an exciting step forward
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-edge machine learning techniques will be used, including Large Language Models (LLMs). About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the
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infrastructure enables recruitment of 200-300 severely injured patients annually as part of the ACIT study. We also have a well-established experimental modelling group with full ethical approvals in place for all
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
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responsibilities will include: Pre-registering data analysis plans; Leading and conducting advanced statistical analyses (e.g., twin/family designs, genomic and epidemiological methods, longitudinal modelling
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of areas. You will join the ATLAS group in the Particle Physics Research Centre, working within our L1Calo team, making leading contributions to the ATLAS calorimeter trigger upgrade for the high
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collaborate with bioinformaticians, experimentalists and clinicians. About You Essential requirements for this post include a PhD in a relevant biological or computational subject and background in
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to their own research interests. About You Candidates should have a PhD in a relevant discipline or will have obtained it by commencement of the position. Candidates should have some experience in multi
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develop, synthesise and characterise materials for this project. About You The post is suited to a PhD graduate with a background in materials chemistry or a related discipline. If you have a vivid
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partners at NHS England and Nuffield Health. About You You will have a PHD (or close to completion) or experience at a comparable level in a relevant subject area. You will have experience of working with