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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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: 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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. •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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sanitation industries. Working with our established industry partners, you'll implement your innovations in real operational environments, seeing your research make tangible difference while building
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
, segmentation, and severity quantification. The performance of AI models will be assessed across different impact energies, materials, and boundary conditions. Cranfield University is uniquely positioned
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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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systems and future telecom solutions. This project aims to design a localisation/positioning framework capable of leveraging signals from terrestrial base stations, non-terrestrial networks (presented by
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type(s)PhD Duration of award3 years EligibilityUK, EU, Rest of world Supervisor Dr. Gustavo Castelluccio is a leader in Mesoscale Mechanics. His work integrates multiscale models and experiments
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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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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