Infrastructure and Environment

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.

Are you interested in pursuing a PhD at the interface of chemical engineering, materials science, and microbiology at the University of Edinburgh? We are seeking a talented, motivated, and curious PhD student to develop innovative strategies for the safe and sustainable reprocessing of reusable medical devices. Reusable medical devices are central to modern healthcare. Their use reduces costs for healthcare systems such as the NHS and minimises environmental impact compared to single-use alternatives. However, their safe reuse depends critically on effective decontamination. While cleaning removes visible contamination, disinfection targets microscopic pathogens from previous patients. These microorganisms often exist as complex, highly resistant biofilm communities that are difficult to eradicate. Current reprocessing methods rely on aggressive physical and chemical treatments, which can unintentionally damage device surfaces. This can lead to microplastic release, as well as the formation of microcracks and surface grooves that promote further bacterial adhesion and resistance.

This project addresses a key challenge:

How can we effectively disinfect reusable medical devices without degrading materials or promoting microbial attachment? You will work with a custom-built laboratory system to simulate decontamination processes in a controlled manner.

This will enable you to:

• Investigate how different cleaning conditions influence surface degradation and microplastic release
• Grow and analyse biofilms on treated surfaces using advanced microscopy techniques
• Explore whether engineered surface patterns can reduce microbial attachment
• Develop and test surface functionalisation strategies to inhibit biofilm formation

This interdisciplinary project combines experimentation, surface engineering, and microbiological analysis. You will also collaborate with leading UK medical device reprocessing companies, ensuring strong real-world impact. As a PhD student, you will benefit from a dynamic research environment and opportunities to present your work at conferences, workshops, and seminars, while building both academic and industry collaborations.

Closing Date 22nd September 2026 

Please note that the position may be filled before the closing date if a suitable candidate is identified. 

Application Documents 

• Curriculum Vitae
• Degree Transcripts and Certificates
• Research Proposal (not more than 3 pages)

For informal enquiries, please contact: eepelle@ed.ac.uk . The University of Edinburgh is committed to equality, diversity, and inclusion, and welcomes applications from all qualified candidates.

• Ozone Decontamination of Medical and Nonmedical Devices: An Assessment of Design and Implementation Considerations: (https://doi.org/10.1021/acs.iecr.2c03754).
• Efficacy of gaseous ozone and UVC radiation against Candida auris biofilms on polystyrene surfaces (https://doi.org/10.1016/j.jece.2024.113862).

• An excellent undergraduate degree (at least a UK 2:1 honours degree, or international equivalent) in Chemical Engineering, Materials Science, Biomedical Engineering, or a related field
• A Master’s degree (MSc/MEng) in a relevant discipline is desirable
• Interest in surface science, interfacial engineering, microbiology, and laboratory-based research
• Strong analytical and problem-solving skills
• Experience with microscopy techniques (SEM, TEM, AFM) is advantageous

This project is currently open to self-funded applicants. You will be embedded within a highly supportive and well-resourced research environment at the University of Edinburgh, with access to state-of-the-art laboratory facilities. Exceptional candidates will also be supported in applying for competitive external funding opportunities, scholarships, and sponsorships as they arise.

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.