Department of Engineering
This is a four-year (1+3 MRes/PhD) studentship funded through the Cambridge EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Unlocking Net Zero (FIBE3 CDT). Further details can be found at https://www.net-zero-fibe-cdt.eng.cam.ac.uk/
The project is funded in collaboration with Network Rail, the entity responsible for the operation and maintenance of the Great Britain's railway infrastructure, with an extensive network spanning thousands of miles and its complex web of tracks, stations, signalling systems and more, and is fully committed to advancing research and innovation in the field of infrastructure and built environment to enhance efficiency, safety and sustainability.
As we navigate 2026, the global rail industry faces a systemic paradox: an unprecedented abundance of sensor data juxtaposed against rapidly aging infrastructure and increasingly constrained capital budgets. Traditional track renewal specifications remain siloed and manual, often failing to account for the non-linear impacts of climate resilience, carbon efficiency, and long-term lifecycle costs. To maintain a sustainable network, we must transcend "predictive" maintenance-which merely forecasts failure-and move toward "prescriptive" asset management.
This project aims to develop a pioneering Generative AI framework designed to autonomously synthesize and optimize capital investment strategies. By leveraging Large Language Models (LLMs) and Graph Neural Networks, the candidate will create a system capable of interpreting complex engineering standards, historical maintenance logs, and real-time telemetry to generate high-fidelity renewal specifications.