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Sh.24.3 PhD in Sound Analysis for Predicting Category 1 Ambulance Calls School of Computer Science PhD Research Project Competition Funded UK Students Dr Ning Ma, Prof Jon Barker Application
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Department: Materials Title: Predicting and Improving the Quality of Recycled Plastics Using Advanced Metrology and Data Science Application deadline: 15/09/2025 Research theme: "Materials
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. The start date is October 2025. Are you passionate about applying computational science to real-world engineering problems? Do you want to develop digital twins of materials that can predict performance and
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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The project: We invite applications for a fully funded PhD studentship in the Solid Mechanics Group at the University of Bristol to work on the predictive modeling of hydrogen-induced damage in
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population-based studies. Using data from 11 million vessel segments from over 200,000 CFPs, we have shown that retinal vasculometry can predict cardiovascular disease as well as established risk scores and
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
for training and conferences, and includes a placement with Rolls-Royce. This project focuses on advancing digital twins with AI-driven reasoning for predictive maintenance in aerospace systems. While aircraft
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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(synoptic scale) forecasting (< 5 days) to mid-century scenario-based climate predictions (time horizon 2030-2050). The project will explore the merging of data sources to estimate future energy requirements