11 postdoc-in-automation-and-control PhD positions at Cranfield University in United Kingdom
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We are looking for a highly motivated candidate to pursue a PhD programme titled "CFD-informed finite element analysis for thermal control in wire-arc directed energy deposition." This research
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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project will develop novel methods for modelling and controlling large gossamer satellites (LGSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. The candidate will
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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aerospace, healthcare, and industrial automation. In safety-critical domains, such as aviation and medical devices, rigorous certification processes and continuous lifecycle monitoring are essential to ensure
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significant relevance in today's technological landscape. As industries continue to integrate digital and physical systems, the role of eCPS in enhancing automation, control, and sustainability becomes
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and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive
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Instrumentation and Environmental Control Systems (AID, ECS): A full-scale Boeing 737-400 aircraft equipped with instrumentation for studying environmental control systems and other onboard systems, providing a
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
) to engineers and automated systems •Validate the system’s resilience, scalability, and practical relevance using real-world and representative datasets, with evaluation of technical performance and potential