AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD

Overview:

Cranfield University invites applications for a fully funded 3-year PhD, supported by the EPSRC DTP and Rolls-Royce. This studentship covers tuition, a tax-free stipend, funding for training and conferences, and includes a placement with Rolls-Royce.

This project focuses on advancing digital twins with AI-driven reasoning for predictive maintenance in aerospace systems. While aircraft generate vast amounts of operational and maintenance data, much of it remains fragmented and underutilized. Unlocking insights from this unstructured data could enable earlier fault detection, improved system health monitoring, and more efficient maintenance planning.

Digital twins offer a powerful foundation but must evolve beyond simulation to truly support engineering decisions. This PhD will develop intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins that not only simulate, but also reason, adapt, and provide explainable insights across safety-critical environments.

Key research objectives include:

• Extract structured engineering knowledge from unstructured maintenance records using LLMs, ontologies, and knowledge graphs.
• Build a standards-aligned semantic framework for interoperability and scalability.
• Model degradation over time using temporal knowledge graphs for predictive insights.
• Design feedback mechanisms to deliver interpretable outputs such as alerts, recommendations, and confidence scores.
• Validate system robustness using real-world datasets with industry-aligned performance metrics.

The successful candidate will join the Centre for Digital and Design Engineering (CDDE), part of Cranfield’s Manufacturing, Materials and Design theme. CDDE provides access to advanced simulation tools and a collaborative environment focused on AI, digital twins, and immersive technologies.

This project is co-sponsored by Rolls-Royce, offering unique access to domain expertise, real-world data, and a pathway for industrial impact. A 3-month placement with the sponsor will provide practical experience in deploying digital twin technologies in aerospace.

Benefits include:

• Fully funded studentship with living stipend
• Industry placement and access to Rolls-Royce data and expertise
• Conference participation and specialist training in AI and digital engineering
• Close collaboration with academic and industry leaders
• Development of technical and professional skills for careers in aerospace, AI, and digital transformation

This PhD offers a unique opportunity to contribute to next-generation maintenance strategies through cutting-edge AI research with real industrial relevance.

Application Deadline: 16 Jul 2025

Start date: 29 Sep 2025

Supervisor

1st Supervisor: Dr Christina Latsou

2nd Supervisors: Professor John Erkoyuncu, Dr Bernadin Namoano

Entry requirements

Applicants should hold a First or Upper Second Class UK Honours degree (or international equivalent) in a relevant field such as engineering, computer science, or applied mathematics. Experience or interest in AI, machine learning, or digital systems is beneficial. We welcome candidates from diverse backgrounds, including those with industry experience or non-traditional qualifications. Applications are encouraged from underrepresented groups, and flexible study options and tailored support are available. 

Funding

Sponsored by the EPSRC Doctoral Training Partnership and Rolls-Royce plc, this opportunity provides a fully funded 3-year full-time PhD with a £25,000 tax-free annual stipend, payment of tuition fees, and additional funding for international and national conferences, training, and industrial placement.

How to apply

For further information please contact:

Name: Dr Christina Latsou                     
Email: Christina.Latsou@cranfield.ac.uk

If you are eligible to apply for this studentship, please complete the online application form via the above ‘Apply’ button.

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