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Verification Tools: Develop AI algorithms that automate the verification process, ensuring systems meet required safety and performance standards. Health Monitoring Algorithms: Implement AI-based monitoring
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electronic systems. This PhD project aims to develop intelligent electronic systems capable of autonomous fault detection and self-repair. The research will investigate AI-driven methodologies for predictive
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the challenge of forever chemicals in drinking water. The aim of this research is to develop a smart data predictive model that will support utilities’ evidence-based decision-making to improve the resilience and
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
, this project contributes to advancing smart materials diagnostics, supporting sustainability, safety, and technological competitiveness in key engineering sectors. To develop an AI-driven methodology
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it can also increases the electrical conductivity of composites. As a part of this research project we will develop joint UK projects with aerospace, automotive, marine, and wind turbine manufacturers
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global leader for transformational research and education in technology and management. Research Excellence Framework 2014 (REF) has recognised 81% of Cranfield’s research as world leading
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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
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project will develop novel methods for modelling and controlling large space structures (LSSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. Working with leading
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, which enhance our teaching and research. Our specialist areas of focus, or Cranfield themes, are where we bring a range of academic disciplines together in order to tackle the grand challenges facing
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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