Civil and Environmental Engineering

This PhD project will develop data-driven digital twin methods for predicting climate-driven deterioration in UK bridges. The project will combine civil engineering, machine learning, statistical modelling, and climate resilience to address an important challenge in infrastructure asset management.

Many UK bridges are ageing and are increasingly exposed to changing environmental and climate conditions. More intense rainfall, flooding, temperature variation, freeze-thaw effects, and extreme weather events can accelerate deterioration and increase uncertainty in long-term maintenance planning. Although bridge inspections generate valuable condition, defect, maintenance, and asset data, these records are often underused for predictive modelling and strategic decision support.

The successful candidate will explore how historical bridge inspection records, defect classifications, condition ratings, maintenance histories, structural characteristics, and environmental exposure data can be brought together to understand how bridge condition changes over time. The project will also investigate how climate-related variables may influence future deterioration patterns.

The main research aim is to create dynamic, asset-level digital twin models that can support bridge condition forecasting. These models will be designed to update as new data become available, helping to move bridge assessment from static inspection records towards more predictive and evidence-based asset management.

The project will examine both fully data-driven and hybrid modelling approaches. This may include machine learning, statistical deterioration models, uncertainty quantification, and physics-informed methods that incorporate engineering understanding of bridge behaviour and deterioration mechanisms. A strong focus will be placed on model interpretability, data limitations, uncertainty, and the practical value of the outputs for infrastructure owners and decision-makers.

The project is suitable for applicants with a background in civil engineering, structural engineering, data science, computer science, or a closely related field. The final research direction will be refined with the successful candidate, depending on their skills, experience, and interests.

Applicants should have:

A 2:1 undergraduate degree, or equivalent, in civil engineering, structural engineering, mechanical engineering, or another closely related engineering discipline.

Strong programming and data analysis skills, preferably using Python.

Demonstrated experience or strong knowledge of machine learning, statistical modelling, data-driven modelling, or digital twin development.

A clear interest in bridges, infrastructure systems, asset management, and climate-related deterioration.

The ability to work independently and as part of a multidisciplinary research team.

Good written and verbal communication skills.

The following would be desirable:

A Master’s degree, or equivalent, in civil engineering, structural engineering, mechanical engineering, infrastructure engineering, or a closely related engineering discipline.

Previous experience with bridge inspection data, structural assessment, deterioration modelling, climate impact assessment, digital twins, or infrastructure asset management.

Duration: 42 months (3.5 years)

Start date: Flexible, but no later than July 2027

Stipend: The studentship covers tuition fees and provides a tax-free stipend for the full duration of the award. The stipend rate for academic year 2026/27 is £22,483.

Further information and other funding options.

This PhD tackles a fundamental open problem in soft matter physics that sits at the heart of multiple industries and natural systems: the rheology of dense suspensions of elongated particles. Why does a slurry packed with cellulose fibres flow so differently from one packed with spherical particles? What common principles govern the flow and mechanical behaviour of crystal-laden lavas, river logjams, recycled carbon-fibre composites and bacterial suspensions, and how do we develop predictive models useful to real world practitioners? Despite decades of progress on dense suspensions of spheres, culminating in unified flow laws and quantitative theories of jamming, an equivalent description for rod-shaped particles do not yet exist. You will help build it. The project will combine particle-based simulation with continuum modelling to deliver the first physics based constitutive model for dense rod suspensions, resolving how alignment, packing fraction and heterogeneous flow interact to produce stress. You will work at an active frontier of contemporary soft matter physics, joining a group with a strong international profile and an active track record of publishing in Physical Review Letters, Journal of Fluid Mechanics, and other important journals. The science is genuinely fundamental, but its applications are immediate: your insights will feed directly into our basic understanding of manufacturing process such as speciality chemical crystallisation, composite recycling for the circular economy, and volcanic hazard prediction. You will become fluent in modern computational soft matter, writing and deploying GPU-based particle simulation codes; utilising Edinburgh's Eddie cluster and ARCHER2; statistical analyses of high-dimensional simulation data; and continuum modelling. You will have the opportunity to write your own codes from scratch and to use standard open source codes such as LAMMPS and OpenFOAM. You will graduate with a skillset that maps onto careers in academic research, computational materials science, engineering R&D and quantitative industry roles. You will be supported by Dr Chris Ness, Reader in Chemical Engineering, who runs an active group with a strong record of researcher development. You will be embedded in an international collaborator network, will attend major conferences in the field, and will contribute to open-source software releases that the wider community will use. We are seeking a motivated graduate in physics, applied maths, mechanical or chemical engineering, materials science or a related discipline, with an insatiable curiosity about how things flow. Prior simulation experience is welcome but not required.

Minimum entry requirements

• a 2:1 undergraduate degree (or equivalent).
• the University’s English language requirements.

Funding may be available for this project, please enquire.

Further information and other funding options.