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Field
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scenarios. By enabling more realistic and dynamic adversarial simulations this project will support the creation of more effective cybersecurity testing, consequently strengthening organisational resilience
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Discipline: Engineering & Technology, Civil Engineering Qualification: Doctor of Philosophy in Engineering (PhD) Eligibility: All qualified candidates (both UK and International students) Award
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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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, 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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interdisciplinary training in AI, modelling, and data analytics Contribute to real-world engineering applications Be part of the dynamic research community at the Zienkiewicz Institute for Modelling, Data and AI
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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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overcomes the geographic limitations of conventional systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing
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, lack of transparency, safety assurance, and sustainability. You will work at the forefront of AI research, exploring formal and dynamic verification methods, explainable AI, and data space integration
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load emulation, surface tribology and lubricants, contact mechanics or dynamical phenomena. This is an opportunity to work within a world-class multidisciplinary team within the Engineering Systems
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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