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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems
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maintenance. However, current technologies are relatively slow and not capable enough to provide quick performance, diagnostic and prognostic predictions for real time applications. With the rapid development
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on previous research at Cranfield, which has demonstrated the benefits, the project investigates the impact of various porous structures on aerodynamic performance. Focus is placed on the entire incompressible
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. •Detailed semantic understanding of operational environments for Machine Situational Awareness, particularly within contested, congested and degraded scenarios. •Fully autonomous robust intelligence data
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trustworthy operation of navigation systems in complex, GNSS-denied scenarios. The ultimate goal is to provide the navigation research community and industry with tools and methods that ensure continuous, high
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frameworks are critical to ensuring safe, resilient, and trustworthy navigation in transport and other domains. This project will aim to enhance the performance and robustness of autonomous navigation
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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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more sensitive and faster cancer imaging. This PhD project will focus on surface functionalisation of metascintillators to optimise their scintillation performance, light yield, timing resolution, and
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a comprehensive, multi-fidelity suite of liquid hydrogen (LH2) pump models to predict and analyze pump performance, stability, and its interaction with the broader fuel system architecture for a