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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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The Structural Battery Company, a high-tech manufacturer of EV batteries. Building on Cranfield’s previous APC-funded CERABEV successes using epoxy-based systems with intumescent ceramic phases, this project
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
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Nuclear fusion offers the prospect of clean, abundant, and safe energy that could transform global energy systems. Achieving this goal depends on materials that can endure extreme environments
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testing) to understand and tailor the physical and chemical interactions within these complex structures. Cranfield University is internationally renowned for its research into materials for extreme
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slow sand filters. This project suits graduates seeking careers in drinking water technology, sustainable infrastructure, and low carbon process design. Drinking water production is under mounting
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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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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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. The control systems developed will be designed to handle these complex dynamics, feeding directly into industrial SBSP and related designs, thus enabling their longer-term plans for commercial SBSP capable
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uncertainty and dynamic conditions. In complex electronic systems, ensuring reliability and minimizing downtime are critical challenges. AI-driven fault diagnosis and self-healing electronics offer innovative