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Tomography (ToF-PET) offers vital functional and molecular insights for improved cancer staging, its current capabilities are often limited by the timing resolution and sensitivity of existing detector
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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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-Performance Computing and Future Data Centres AI-Optimized Electronics for Edge and Cloud AI Acceleration – Investigate AI-enhanced data centre electronics, optimizing workload distribution, energy efficiency
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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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explore the nonlinear structural dynamics of LGSs to fully understand the complexity of their control. They will use this foundation to explore idealised and realistic control laws to virtually “stiffen
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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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(technical personnel and toilet users), implementing AI-driven data analytics for system optimization, and creating predictive models for environmental and social impact assessment. Students will work with
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sensors, firmware-controlled automation, wireless connectivity, and maintenance algorithms. Students will design, build, and test smart sanitation solutions that can monitor system performance, optimize
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intelligent systems aim to optimize power usage without compromising performance, employing strategies like power-aware computing and thermal-aware optimization. These systems are crucial in extending