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Field
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interfaces, rheology, fluid dynamics across scales, lubrication and wetting, biophysics as well as machine learning or metamaterials. For experimentalists, we are particularly interested in applicants aligned
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structure prediction tools. This will include development and application of interactive molecular dynamics simulations in virtual reality, and use of design methods based on AI and machine learning. You will
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influence clinical practice. We welcome applications from candidates with following backgrounds: Candidates with strong experience in medical image analysis, machine learning (especially deep learning) and
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situated in the College of Creative Arts, Technology and Engineering. The purpose of the role is to support teaching and learning through the delivery of modules in conjunction with the Course Leader
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training. Teaching and Learning is important to the Department and University and all applicants will be expected to be able to play a full role in teaching across the undergraduate and postgraduate
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of disseminating research to both academic and industrial audiences. Qualifications PhD (or close to completion) in Computer Science, AI, Data Science, Machine Learning, Edge Computing, Signal Processing
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machine learning and artificial intelligence. The successful candidate will support teaching on a new postgraduate MSc in Data Science with Machine Learning and AI, as well as more general teaching
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, Nutritional Sciences and Women's Health cluster) for REF was rated as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research
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opportunity to work at the intersection of multimodal AI, cancer biology and digital pathology. The successful applicant will have a PhD or MSc with a significant machine learning element and/or experience with
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a range of methods including causal inference, signal processing and machine learning to investigate whether and how acceleration data from flying birds can be used to characterise changing levels