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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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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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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
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sensors - if we can control and tune their properties. You will develop and use top-of-the-line machine learning models to predict the sensor response of these materials under realistic conditions
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, machine learning, molecular dynamics, and fluid mechanics. We aim to understand how chemical structure of precursors and process conditions affect film quality, helping design better materials and
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industry and academia together to drive pre-competitive, fundamental research in polymers. We welcome applicants with interests in polymer physics, materials processing and characterisation, machine learning
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of students and can include, technique development, microscopy-spectroscopy, analysis/programming (including AI and machine learning) and materials-focused studies. We use innovative high-resolutionidentical