798 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions in United Kingdom
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UKESM1 or similar models, advanced data analysis and machine learning, would be advantageous. Grade E: You will be near completion of a relevant PhD or have equivalent research experience, and be able
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Machine learning/AI based classifiers Proficiency in coding using R and Python and other similar languages High level analytical capability Ability to communicate complex information clearly Informal
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: Genomics, precision medicine, bioengineering, and health data science AI and Digital: Machine learning, robotics, digital health, and cybersecurity Defence and Advanced Manufacturing: Secure systems
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during term time. Research Fellows benefit from up to £10,000 in additional grants over their four-year tenure. These grants support the costs of academic materials, travel expenses, computer equipment
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testing of machine learning/AI algorithms Integration of radiomic and biological datasets Working closely with Medical Physics colleagues on reviewing recommendations for detection of specific metabolites
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to 31 March 2027. The successful applicant will carry out experimental and computational work related to this project. The post will include the following duties: To undertake research, to act as research
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strong analytical skills and desirably some computer modelling experience, and an ability to work in a multidisciplinary team and engage confidently with partners. You will have a track record of
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Flag and is: i) an alert for possible cancer for GPs that appears on primary care blood test results; ii) an accompanying patient management pathway. This post is funded by the NHS Cancer Programme with
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spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological
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spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological