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on qualifications and relevant skills acquired and will also be determined by the funding available. About you Applicants will hold a PhD/DPhil or be near completion of a PhD/DPhil in a subject relative to Structural
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economic studies funded by the UK National Institute for Health and Care Research. Experience of conducting economic evaluations using suitable statistical software (e.g. STATA, R or SAS) is essential
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learning approaches. You will develop novel, reproducible methods for analysing both structured and unstructured clinical data, generating insights into disease trajectories, predicting clinical outcomes
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health data, development and testing of solutions using rigorous methods of evaluation recognised in Global Health, and a focus on highly scalable, cost-effective solutions that can be delivered at scale
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libraries (TensorFlow, Keras, and PyTorch), strong programming skills (Python and R), experience with statistical and mathematical methods for AI in high-dimensional spaces, and proficiency in developing
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), to develop systems that improve the efficacy of machine learning-based technologies for healthcare applications. You must hold a PhD (or be near completion) in a field such as AI, computer science, signal
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correspondence. As part of your formal online application, you will be required to upload a CV and supporting statement. Only applications received before midday on 25 September 2025 can be considered.
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modern privacy-enhancing technologies (e.g. based upon synthetic data or using formal differential privacy guarantees) impact research integrity and reproducibility. This is an exciting line of research
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become an embedded member of both teams, joining a Senior Research Fellow and PhD student who engage in cross-technique research in a clinical setting. The hyperpolarised groups are strategically placed
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, available for up to 30 months, tenable immediately, to conclude before May 31st 2028. Intelligent agents have a venerable history in AI: since the 1980s the problem of how to build hardware and/or software