Application Deadline: 28 February 2026
Details
The University of Sheffield, Sheffield Water Centre in collaboration with Network Rail and the EPSRC Centre for Doctoral Training in Water Infrastructure and Resilience.
PhD Studentship in: Water and Vegetation Driven Failure Modes on Railway Infrastructure
Stipend: This post will fully cover university tuition and provide a tax-free stipend for Home and Overseas students of £25,726 per year.
Applications Close: 28th February 2026
Start Date: 28th September 2026 (contract duration 4 years)
Railway performance across the UK is increasingly shaped by how water, climate and vegetation interact. Flooding, saturated embankments, blocked drains and seasonal leaf-fall all contribute to safety risks, delays and costly maintenance interventions. At the same time, rising temperatures, wetter autumns and higher climate variability are changing vegetation behaviour, driving new and more frequent failure modes across the network. Network Rail now manages thousands of kilometres of drains, culverts, cuttings and embankments that sit at the centre of this challenge. Understanding how water and vegetation jointly influence infrastructure reliability is essential to delivering a safer, more resilient and more sustainable railway.
This PhD project will establish the first integrated national-scale analysis of hydro-vegetation risk across railway infrastructure. Working directly with Network Rail engineers and data scientists, the successful candidate will combine data such as drainage and earthworks records, vegetation surveys, inspection histories, operational performance data, hyperspectral imagery, train-borne video analytics and satellite soil-moisture products to build predictive models of vegetation-driven and water-driven failures. By fusing these datasets with rainfall, topographic, soil-moisture and climate-projection data (UKCP18), the project will reveal how rainfall patterns and drainage condition influence vegetation growth, canopy dynamics, root ingress and leaf-fall timing – and, in turn, how vegetation affects blockage, flooding and instability.
The research will examine both directions of the feedback loop: how hydro-climatic conditions govern vegetation behaviour, and how vegetation impairs the functioning of drainage and water-management assets. Using advanced geospatial modelling, machine learning and digital-twin concepts, the project will identify hotspots where vegetation, drainage and rainfall combine to create compound risks. It will then project these hazards into the future using climate scenarios, generating evidence to support long-term climate adaptation and investment planning.
Students will build a comprehensive set of high-value technical and professional skills, including:
• Geospatial and GIS analysis.
• Hydrological and hydraulic simulation.
• Machine learning, including unsupervised clustering and predictive modelling.
• Working with large, complex, multi-source datasets using MATLAB, Python and associated data-science libraries.
• Climate scenario analysis and integration of UKCP18 projections.
• Asset-management analytics and digital-twin concepts for infrastructure.
• Communicating technical findings to both academic and industry audiences.
• Collaborative working with engineers, data scientists, field teams and decision-makers.
These skills are highly sought after in rail engineering, infrastructure consultancies, water companies, environment agencies, transport operators and the wider climate-resilience sector. The combination of domain knowledge (water, vegetation, drainage, asset performance), strong quantitative and computational skills, and experience working directly with a major national infrastructure operator will significantly enhance the student’s career prospects. Graduates from similar projects typically move into influential roles in data-driven engineering, infrastructure resilience, environmental risk analysis, or directly into Network Rail’s technical and analytical teams.
Because this project is co-developed with Network Rail, the student will work closely with industry throughout the four-year programme. There will be opportunities for industry placements, embedded periods with regional or national teams, technical workshops, and site visits to understand real-world drainage and vegetation-management challenges. These placements will expose the student to operational practices, risk-assessment frameworks, and the practical constraints of managing a national transport system – experience that is rarely available within a purely academic PhD.
The outcomes of the project will include new predictive tools for drainage and vegetation-related failures, national-scale risk clusters, and prototype decision-support dashboards for route engineers and asset managers. The research has strong potential for impact, aligning closely with Network Rail’s CP7 goals for climate adaptation, predictive maintenance and digital-asset optimisation. The modelling framework developed may also be transferable to other linear infrastructure sectors, including highways, pipelines, flood defences and water-industry assets.
This project offers an excellent opportunity for a motivated student to tackle a real-world engineering challenge at the interface between climate, water, vegetation and infrastructure – and to develop skills that are directly aligned with the future demands of the transport, water and environmental sectors.
The research programme to be completed in this project will be undertaken as part of the EPSRC Centre for Doctoral Training in Water Infrastructure and Resilience (CDT WIRe). WIRe is a collaboration between the three leading UK Universities in water resilient infrastructure. Students will benefit from a bespoke training scheme delivered by world leading experts from academia and industry, access to world leading experimental and computational facilities as well as close and regular contact with industry and end user partners. WIRe is committed to promoting a diverse and inclusive community, and offer a range of family friendly, inclusive employment policies. For further information on the WIRe scheme visit: https://cdtwire.com/
The project will be supervised by Dr Andy Nichols, Professor Simon Tait and Dr Yiqi Wu, in collaboration with partners from Network Rail. There will be generous opportunities to travel to visit our academic and industry partners in both the UK and overseas.
Eligibility Criteria
This studentship is subject to standard RCUK eligibility criteria. It is open to students with Home or Overseas residency (subject to a maximum quota of overseas students per training grant).
The selection criteria for the position are;
• A good honours degree (or equivalent experience) in Engineering, Physical Science, Mathematics, Computer Science or related subject.
• Enthusiasm for research
• Good level of written and oral communication skills, as appropriate for disseminating research and communicating with project partners.
• Willingness and ability to collaborate with other researchers, industry and end-users.
• Aptitude in a relevant area (e.g. data analysis, machine learning, rail engineering, asset management, hydrology, hydraulics) as evidenced by previous experience.
How to apply
Interested candidates should email a covering letter and Curriculum Vitae to Lindsay Hopcroft (cdtwireapps@sheffield.ac.uk). For informal enquiries please contact: Dr Andy Nichols (a.nichols@sheffield.ac.uk)
Funding Notes
Stipend: This post will fully cover university tuition and provide a tax-free stipend for Home and Overseas students of £25,726 per year.
This studentship is subject to standard RCUK eligibility criteria. It is open to all students with Home or Overseas residency (subject to a maximum quota of overseas students per training grant).
