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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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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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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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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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(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
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Title: Predicting and Improving the Quality of Recycled Plastics Using Advanced Metrology and Data Science Research theme: "Materials Characterisation" "Data Science and Machine Learning in
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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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model of the lower airways, multi-omic analyses will provide a key tool to characterise mechanistic changes and assist in predicting the human health impact of the exposure to different air pollutants
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Fully-funded PhD Studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics This exciting opportunity is based within the Fluids and Thermal Engineering
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of investigation, many predictive tools lack robust ways to incorporate uncertainties in boundary conditions, turbulence modelling, and manufacturing variability. Problem Statement Conventional CFD workflows assume