41 software-engineering-model-driven-engineering-phd-position PhD positions at Cranfield University in United Kingdom
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. This PhD project will tackle that challenge by developing intelligent methods that combine AI techniques such as language models that interpret technical text and knowledge graphs that map engineering
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Cranfield’s Advanced Vehicle Engineering Centre is inviting applications to study for a PhD in battery modelling and management for electric vehicles. Several projects are on offer, covering
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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combination of experimental testing and computational modelling (Finite Element Analysis) to create solutions that accelerate the safe deployment of hydrogen aviation technologies. This position is part of
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Multiple self-funded PhD positions are available in Modelling and Simulation (M&S). The project will aim to mature software repositories describing the biomechanics of the human brain. The M&S tools
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sustainable aerospace technologies. Hydrogen-powered flight is set to revolutionise aviation, offering a sustainable path toward achieving Net Zero by 2050. The key enabling technology for a hydrogen fuelled
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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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complex in scenarios like parallel-pass deposition, thin-wall deposition, and off-centre or out-of-position deposition. Additionally, FEA models are focused on thermal conduction in solid medium and often
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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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integrating Machine Learning (ML) with physics-based degradation modelling will enhance early fault detection, reducing unplanned downtime. This PhD is hosted at Cranfield University, a global leader in