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Field
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, biomedical engineering, medical imaging, or related field. Experience in deep learning with practical implementation. Strong Python skills and relevant frameworks. Experience with large clinical imaging
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of Edinburgh, with eight groups across the UK and the United States, the consortium engineers microbial S-layer proteins into large-area, defect-free, metal-coordinated 2D lattices for selective ion separation
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alerts that flag injury risk during the training season. Vet Vision AI has developed this technology to characterise behaviour in healthy horses and to detect patterns associated with disease in
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out with human blood samples including multiplexed serology (using bead-based Luminex technology), ELISA, flow cytometry, and sterile cell culture. Knowledge of R or STATA packages for large dataset
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modelling, health technology assessment, or a related area, together with strong quantitative and analytical skills. Experience conducting economic evaluations and analysing complex datasets using statistical
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quantitative discipline, and will have experience in health economics, disease modelling, health technology assessment, or a related area, together with strong quantitative and analytical skills. Experience
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and performance studies, digital humanities, games and interactive media, media literacy, educational technology, participatory arts research, or related fields, who can demonstrate innovative
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science, or a Ph.D. with a computational profile (computer science, engineering, applied mathematics, or natural science) and active experience with interdisciplinary collaborations with the previous fields
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on the ‘ADHD Remote Technology and ADHD transition: predicting and preventing negative outcomes’ (ART-transition) project. The post-holder will work with Professor Jonna Kuntsi and Dr Aislinn Bowler
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digital technology providers and European welfare states in the context of data-driven innovation. The candidate will lead the work package on ‘infrastructures’ that examines the functions and features