30 phd-studenship-in-computer-vision-and-machine-learning PhD 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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chemistry modelling techniques scientific machine learning high-performance computing molecular design, generative AI, database handling and analysis collaborative, project management, presentation 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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Funding for: UK Students Research area and project description: Develop scalable acoustic methods to structure advanced polymer composites for lightweight, low‑carbon technologies. This PhD explores
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. This PhD project aims to create advanced XCT workflows by developing Artificial Intelligence (AI) and Machine Learning (ML) tools to support imaging before the reconstruction phase. The research will focus
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-ray Computed Tomography (XCT) has evolved into a significant "big data" challenge, with a single scanner easily generating over 10TB of data annually. The sheer volume of this structured data creates
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and AI Infrastructure priorities, this PhD project pioneers fundamental technologies of Solid-State Transformers to enable highly efficient, reliable, and bi-directional energy flow control. You will
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ignition products, with future opportunities expected in data storage devices and electrical contacts to supply the demand for higher computational power for AI driven technologies. However, the limited
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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