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inference methodology, to undertake research combining state-of-the-art machine learning and causal inference for solving real world healthcare challenges, such as heterogeneous treatment effect estimation in
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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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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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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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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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, 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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(UX) research in digital health Understanding of machine learning, AI, or big data analytics applied to health apps Experience working on NHS-funded digital health projects or collaborations Knowledge
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to scientific journals. The work will establish a high-content / high-throughput microscopy assay in zebrafish embryos enabled by machine learning, and would suit an individual with a background in AI-based