About the Project
A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.
Supervised by Dr Andy Nichols and Dr Giuliano Punzo.
The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.
Project Description
Climate change is creating new challenges for the railway. More frequent and intense rainfall, extreme heat and other weather hazards can affect infrastructure that is already ageing and operating under increasing demands. When infrastructure fails, the consequences can extend far beyond the affected asset, causing delays, cancellations, safety risks and substantial financial costs.
At the same time, the railway is becoming increasingly digital. Network Rail collects large quantities of information about its infrastructure through inspections, monitoring systems and operational records, while predictive models can help us understand how assets deteriorate, respond to extreme weather and ultimately fail. However, these data and models are generally considered separately. Bringing them together offers an opportunity to understand how risk is developing across the railway and anticipate its consequences before failures occur.
This PhD will address that challenge by developing an integrated digital twin framework for railway infrastructure.
A digital twin is a dynamic virtual representation of a real-world system that can be updated as new information becomes available and can inform real-world decisions. You will investigate how different sources of infrastructure data and predictive models can be brought together to create an evolving picture of railway risk. Rather than developing a digital representation of a single asset, you will explore how a digital twin can connect information about asset condition and degradation, climate-driven hazards, interactions between infrastructure systems and the consequences for railway operations.
The University of Sheffield and Network Rail have an established research partnership and have already co-developed models for aspects of railway infrastructure degradation, climate risk, asset performance and intervention planning. These provide a strong foundation for the project. Your research will investigate how existing and future models can be integrated within a flexible digital twin architecture, how predictions can be updated using new monitoring and inspection data, and how uncertainty in data and predictions can be represented and communicated.
You will then investigate how changes or failures in infrastructure can propagate through interconnected systems and affect railway operations. For example, how might deterioration of drainage infrastructure combined with extreme rainfall increase the risk of flooding, affect other railway assets and ultimately cause disruption to train services? The framework will be demonstrated and validated using real railway data and case studies.
This is a collaborative PhD with Network Rail, providing a rare opportunity to conduct academically rigorous research while working directly with the organisation responsible for managing Britain's national railway infrastructure. You will have access to relevant industry data and expertise and will undertake two placements with Network Rail, giving you first-hand experience of how infrastructure risks are managed and how research can influence real investment and operational decisions.
You will develop interdisciplinary skills spanning infrastructure engineering, data analysis, computational modelling, climate resilience and digital twins. Depending on your background and interests, there will also be opportunities to develop skills in areas such as machine learning, uncertainty analysis and visualisation.
You will join an active research environment at the University of Sheffield and work with researchers and industry specialists developing new approaches to infrastructure resilience and asset management. The project offers opportunities for academic publications, conference participation and engagement with the rail industry.
Ultimately, the research aims to help move railway infrastructure management from fragmented and reactive approaches towards integrated, predictive and risk-informed decision-making, supporting a safer, more reliable and climate-resilient railway.
How to apply
Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application.
Please refer to the EPSRC DLA webpage for detailed information about the EPSRC DLA and how to apply. Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.
The application deadline is 17:00 GMT 13 January 2027.
The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.
Funding Notes
The award will fund the full tuition fee and a maintenance stipend at the UKRI rate (currently £21,805 per annum) for 3.5 years, as well as a research grant to support costs associated with the project.
