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Field
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
for wider industry adoption Cranfield University is a specialist postgraduate institution with a strong track record of applied research and close industry collaboration. The successful candidate will be
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and Britain are world leaders and major exporters. This high technology global industry is worth more than £30 billion per annum. Current challenges are arising from the need to address environmental
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generations of research and development professionals, data specialists, technology experts, inventors, and scientists for industry and society. The Macroscopic Quantum Optics (MQO) Group at the Department
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current legislation for non-deterministic AI systems. (2) Create new compliance testing procedures and processes for agricultural AI machine learning systems. These are essential for manufacturers
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-harvesting technologies to promote sustainability. Cranfield University offers a distinctive research environment renowned for its world-class programmes, cutting-edge facilities, and strong industry
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Embark on a thrilling, fully funded four-year PhD journey, with an enhanced stipend of £25,726 per year, and deliver new evidence on how nature-based solutions can reduce the occurrence and mitigate
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engineers Opportunities to work with industry leaders at John Crane Ltd Access to cutting-edge lab facilities and real-world data A platform to publish in top journals and attend international conferences Why
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, Technology & Environment, and Society & Professions Practical experience: Access to industry partnerships, civic engagement initiatives, and applied research projects that make real impact. Comprehensive
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. The lack of clarity and uniformity has resulted in widespread “greenwashing,” with brands and manufacturers making misleading claims about the biodegradability of their products. Without clear guidelines, it
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impact. Recycled plastics often suffer from high variability, making it challenging for industry to predict and optimise their performance for specific applications. This PhD project seeks to address