13 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at University of Warwick
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About the project: Supervisor: Professor Nicholas Hine, University of Warwick This project uses cutting-edge computational and machine learning methods to accelerate catalyst discovery for fuel cell
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About the project: Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy Supervisor: Dr Sara Sangtarash, University of Warwick Thermoelectric materials convert heat
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devices for medical imaging and reaction monitoring, as well as for the development of sustainable photocatalysts. In this role you will develop machine learning (ML)-accelerated quantum mechanics in
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their skills and interests in this research area to https://warwick.ac.uk/fac/sci/eng/postgraduate/funding/ot_epsrc/app/ via the above 'Apply' button. If this initial application is successful, we will invite
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this research area to https://www.warwick.ac.uk/engineeringscholarships/ng_epsrc/app via the above 'Apply' button. If this initial application is successful, we will invite you to submit a formal application
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not essential How to apply: Interested candidates should submit an expression of interest by sending a CV and supporting statement outlining their skills and interests in this research area to https
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on basic laparoscopic surgery tasks, using data collected under varying network conditions and applying machine learning and time-series modelling to predict delay. The models will be integrated into a real
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experts at University Hospitals Coventry & Warwickshire/NHS Trust. The research will involve emulating laparoscopic surgical tasks using a robotic platform under varying network conditions. Machine learning
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machine learning techniques, you will identify patient subgroups, improve diagnostic accuracy, and develop a biomarker-based clinical decision support system to assist risk stratification and outcome
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establish a digital route to quantify the segregation behaviour of residual elements at austenite/austenite grain boundaries through atomic-scale simulations, using modern machine learning techniques and in