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of complex, dynamic flows relevant to closely coupled engine aircraft configurations. You’ll join a pioneering multidisciplinary team that values equity, diversity, and inclusion, gaining unique expertise in
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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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or in an academic role. We will help you develop into a dynamic, confident and highly competent researcher with wider transferable skills (communication, project management and leadership) with
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experience in numerical fluid dynamics is beneficial but not essential; determination, curiosity, and a willingness to learn are key attributes we value. Applicants with alternative qualifications, industry
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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will use advanced unsteady computational fluid dynamic methods for the analysis of coupled intake/fan configurations in crosswind and high-incidence conditions. The research will adopt these methods
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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simulating fluid networks and dynamic phenomena for assessing different solutions is a necessity The overall aim of this project is to improve the confidence in fuel system design process for ultra-efficient
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Computing Strategies: Develop algorithms that dynamically adjust computing resources to minimize energy usage without compromising performance. Thermal Management Techniques: Design systems that effectively
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. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network