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Field
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programming (particularly IDEA) or image reconstruction Experience of computational modelling and simulatxion Knowledge of statistical and/or machine learning methods e.g. for 3D imaging data for applications
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reconstruction Experience of computational modelling and simulatxion Knowledge of statistical and/or machine learning methods e.g. for 3D imaging data for applications such as segmentation, registration or lesion
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biology, engineering, biophysics, computer science) and experience in 3D imaging of brain tissue, image segmentation, and handling large datasets. You are comfortable with machine learning and image
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Candidate For this role, we require a talented researcher to undertake both experimental and model research work. Working knowledge of tablet design, imaging data collection and analysis, computer modelling
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vulnerabilities. This role sits at the intersection of AI for security, AI security, and computer architecture, contributing to a first- of-its-kind security framework for next-generation Hw/Sw computing systems
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not yet competitive for 5-year clinician scientist fellowships. This post is designed for applicants with a research interest in machine learning or data science approaches for patient stratification
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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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desirable with a willingness to learn new skills. The post holder will be required to work independently and as part of a team and be computer literate with excellent communication skills. This is an
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processes that use data-driven machine learning. Given the span of the IN-CYPHER programme, we are seeking multiple motivated research fellows. Unique in its scope, we are developing technologies that span
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of subsurface processes. You will be responsible for leading the development of the approach, which could include transferring learning from other geographic regions and data types, machine learning methods