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-edge research and development in EIT-based tactile sensing, machine learning for real-time state estimation, and sensory-motor control optimization for robotic systems. Funding is available for this post
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science, medical statistics or machine learning methods Advanced knowledge of electronic healthcare records and their use in development and validation of risk prediction models Knowledge in application
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, hybrid digital/analogue quantum computation, and quantum machine learning The post holder will join Prof Andrew Green’s research group which studies fundamental aspects of many body quantum dynamics and
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seek an ambitious and self-motivated postdoctoral researcher with a strong background in bioinformatics and/or computer sciences (or related fields). Experience with AI and Machine Learning models in
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of it. We teach statistical science at all levels (undergraduate single/combined honours, service courses, MSc and PhD) and carry out research across a wide range of theoretical and applied areas. In
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or machine learning methods Advanced knowledge of electronic healthcare records and their use in development and validation of risk prediction models Knowledge in application of econometrics in research
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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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governmental or charitable funders. This post is tailored for applicants who have a research interest in developing machine learning or data science approaches for patient stratification and genetic association
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To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD in health data science, medical statistics or machine learning methods
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lines, electrophysiology, sensorimotor behavioural tests and machine learning. The project will include designing and running experiments, recording, analysing and writing up results. The postholder will