Infrastructure and Environment

Bridges in the UK are facing increasing challenges due to aging infrastructure and changing environmental and climate conditions. Rising temperatures, increased rainfall, flooding, and more frequent extreme weather events are expected to accelerate deterioration processes and place additional pressure on bridge asset management systems. At the same time, large volumes of inspection and condition data are routinely collected but are not fully exploited for long-term prediction and decision support.

This PhD project focuses on the development of data-driven digital twin approaches to improve the prediction of climate-driven deterioration in UK bridges. The research will explore how historical bridge inspection records, condition ratings, maintenance data, and environmental or climate exposure information can be integrated to support more reliable condition forecasting. The work will investigate data-driven and hybrid modelling approaches that combine engineering understanding with machine learning techniques, while also considering uncertainty and data limitations.

The project will be jointly supervised by Dr Yavuz Yardim (University of Edinburgh) and Dr Demetris Cotsovos  (Heriot-Watt University), leading to a joint PhD degree from the University of Edinburgh and Heriot-Watt University. The candidate will benefit from access to expertise and facilities across both institutions and will work on research questions that are directly relevant to real-world bridge engineering and infrastructure management practice. 

There may also be opportunities to engage with industry partners and real infrastructure datasets.

The exact research direction will be refined with the successful candidate, depending on their background and interests.

Candidate Profile: We seek a highly motivated individual with: 

- A 2:1 undergraduate degree (or equivalent) in civil engineering, structural engineering, data science, computer science, or a closely related discipline.
- Strong programming and data analysis skills (e.g. Python).
- Demonstrated experience in machine learning or data-driven modelling.
- An interest in bridges, infrastructure systems, and climate-related deterioration.
- The ability to work independently and as part of a multidisciplinary research team.
 

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere

Further information and other funding options.

Tuition fees + stipend are available for applicants who qualify as Home applicants.

Tuition fees + stipend are available for applicants who qualify as Home applicants (International students can  apply, but the funding only covers the Home fee rate)

Funding Details:

Duration: 42 months (3.5 years)

Start date: Flexible, but no later than July 2027

Stipend: Enhanced above UKRI standard rate

Research & Training Grant: £5,000 (UoE) or £3,500 (HWU)

Between 1853 and 2020, approximately 25 billion tonnes of coal were extracted from sites across the UK, leaving behind extensive subsurface voids as the majority of these mines have since ceased operation. Today, the UK hosts around 23,000 abandoned coal mines, all of which require ongoing management by the Mining Remediation Authority due to the cessation of dewatering activities. These vast underground void spaces represent a significant yet underexplored opportunity for large-scale thermal energy storage. Owing to the historical concentration of fossil-fuel industries during the Industrial Revolution, nearly 90% of the UK’s largest urban areas are situated above former mining zones, leading to a unique geographic overlap between locations of high heat demand and potential heat storage capacity. These same urban areas are also major sources of waste heat, suggesting strong potential for thermal energy storage. This PhD project proposes that abandoned coal mines can be fully repurposed as long-term thermal energy storage reservoirs, and aims to rigorously evaluate their technical feasibility. 

The specific objectives are to: (i) develop a robust conceptual and numerical model of heat storage processes in flooded mine environments using COMSOL Multiphysics; (ii) integrate the model with representative UK mine geometries and geological settings; and (iii) conduct scenario-based simulations to identify the controlling thermal–hydraulic processes and assess the practical feasibility of mine-based thermal energy storage systems. The project will be undertaken in full collaboration with TownRock Energy, who will support the research through co-supervision, hosting of the PhD student, and provision of relevant datasets, maps, and software tools. Informal queries from potential applicants can be directed to Dr Melis Sutman (melis.sutman@ed.ac.uk).

Essential Background: - 2.1 or above (or equivalent) in civil engineering, geotechnical engineering, engineering geology or any other closely related subjects. Desirable Background: - An MSc/MEng degree on shallow geothermal energy technologies or mine water geothermal energy - Familiarity with numerical modelling tools such as COMSOL Multiphysics or FEFLOW. - Understanding of heat and mass transfer processes in porous or fractured media.

Further information on English language requirements for EU/Overseas applicants.

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere as well as self-funded students.

Competition (EPSRC) funding may be available for an exceptional candidate. Link below for the further details.

Further information and other funding options.

Autonomous vehicles (AVs) are positioned to transform future transportation, yet their safe deployment remains a critical unsolved challenge. Despite intensive research since 2017, today’s AVs still rely on human supervision, largely due to the difficulty of validating the safety of systems built on complex AI components for perception, prediction, and control. These models struggle with diverse and previously unseen scenarios and provide limited guarantees under out-of-distribution inputs or uncertain training data. The rise of End-to-End learning and Vision-Language-Action further complicates assurance, as traditional interpretable module interfaces are replaced by latent representations that hinder modular testing. Addressing this gap requires new knowledge and methodologies that deliver quantitative, real-time safety guarantees and accountability for AI-driven decisions. By integrating safety gatekeepers that evaluate driving risk and intervening proactively, this research advances a timely and urgent frontier: safeguarded AI for autonomous driving. 

The successful candidate will be supervised by Dr. Pavlos Tafidis and Dr. Cheng Wang from both partner institutions resulting in a joint PhD degree from Heriot-Watt University and the University of Edinburgh. This allows gaining access to cutting-edge facilities and expertise in robotics, AI and autonomous systems in a collaborative research environment across University of Edinburgh and Heriot-Watt University. In addition, the candidate will work closely with industry partners who will provide datasets and a real autonomous driving platform. 

• Open to UK home status candidates only (EU applicants must have settled/pre-settled status or indefinite leave to remain and meet residency requirements.) a 2:1 undergraduate degree (or equivalent).

Candidate Profile: We seek a highly motivated individual with: 

• A strong background in intelligent transportation, smart mobility, robotics, machine learning or autonomous systems. 

• Excellent programming skills (Python, C++, or MATLAB). 

• Ability to work independently and collaboratively in a multidisciplinary team. How to Apply: Please send the following to Dr Pavlos Tafidis (pavlos.tafidis@ed.ac.uk) or Dr Cheng Wang (cheng.wang@hw.ac.uk) no later than 16th January 2026 (5 PM UK time): 

• CV 

• Cover letter outlining your motivation and relevant experience

the University’s English language requirements.

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere

Competition (EPSRC) funding may be available for an exceptional candidate. Link below for the further details.

Further information and other funding options.

Funding Details: 

• Duration: 3.5 years (42 months) 

• Start Date: Flexible, but no later than May 2027 

• Stipend: 10% above UKRI standard rate 

• Research & Training Grant: £3,500 total 

The Discrete Element Method (DEM) enables detailed modelling of granular materials, including industrial bulk solids, by modelling interactions between discrete particles. Limits in available computational power initially restricted this approach to discs, and then spheres and multi-sphere clumps. More recently, non-spherical particle descriptors have been introduced such as cylinders, spherocylinders, superquadrics, potential particles, and polyhedral particles. In all these cases, contact detection and overlap calculation incur significant computational cost compared to sphere-to-sphere contacts.

While it is always possible to locally approximate the contacting particles as spheres of appropriate curvature, the accuracy of such an approximation is unknown, and potentially low. Increasing geometric accuracy without a corresponding accuracy improvement in modelling the mechanical behaviour at the contact therefore leads to unnecessary computational cost.

This PhD project will develop new, adaptive contact physics models that will allow a seamless transition between low and high accuracy/cost modelling, while maintaining an optimal balance between geometric and mechanical detail. These models will be verified using continuum mechanics (finite-element) simulations, and validated through their implementation in a discrete element code.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Applicants must demonstrate an appropriate background in numerical analysis, programming, mechanics of materials and/or computational mathematics.

Further information on English language requirements for EU/Overseas applicants.

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere as well as self-funded students.

Competition (EPSRC) funding may be available for an exceptional candidate. Link below for the further details.

Further information and other funding options.

The Finite Element Method (FEM) is widely used for numerical simulation in Engineering, for example in modelling deformable solids. While significant advances have been made in many aspects of the method, most FEM implementations rely on standard polynomial interpolations and Gaussian numerical integration. Recent results in numerical integration for higher order polynomial FEM interpolations show that significant improvements in computational efficiency can be obtained by developing customised formulations. The effort involved in developing such customised formulations, however, currently limits the applicability of this approach. This PhD project will focus on developing an automated framework for developing, testing, and selecting non-standard interpolation and integration methods leading to highly efficient FEM formulations, adapted to the characteristics of the input problem. Established heuristic optimisation approaches will be complemented by novel AI/agentic techniques to locate optimal formulations across a wide search space. The research in this project will be based on existing preliminary work and proof-of-concept tools using both computer algebra software for symbolic calculations (Maple) and numerical computation software (Matlab, Python or Julia). Specific new results obtained through the automated framework will be implemented and tested within commercial computational mechanics codes, such as the Abaqus FEM software.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Applicants must demonstrate an appropriate background in numerical analysis, programming, mechanics of materials and/or computational mathematics.

Further information on English language requirements for EU/Overseas applicants.

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere as well as self-funded students.

Competition (EPSRC) funding may be available for an exceptional candidate. Link below for the further details.

Further information and other funding options.

We are inviting applications for a PhD position dedicated to the design and efficient operation of datacentres systems from the point of view of building services engineering and impact on energy networks. This project seeks to optimise datacentre’s geographical locations and implementing innovative strategies to manage and recover energy effectively.

Candidates will engage in research that combines building services engineering, environmental data analysis, urban analytics, and sustainability principles. This includes exploring efficient HVAC systems tailored for the unique demands of datacentres and the integration in wider energy networks.

The research will critically evaluate the impact of geographic and climatic factors in datacentre designs to harness maximum renewable energy usage and optimum cooling strategies. It will also explore the impacts from the datacentre to the nearby microclimate conditions and methods for waste heat recovery and its reutilisation within building systems.

This interdisciplinary project is ideal for candidates motivated to innovate in the field of sustainable technologies and with a background in building services engineering, environmental engineering, urban climate, or geographical information systems and urban analytics. As such, it will be supervised by an interdisciplinary team, with Dr Daniel Fosas and Dr Desen Kirli from the School of Engineering at The University of Edinburgh, and Professor Qunshan Zhao from the University of Glasgow.

A comprehensive training programme will be provided comprising both specialist scientific training and generic transferable and professional skills. The PhD candidate will be introduced to comprehensive training options. The candidate will have the opportunity to become a teaching assistant following formal training, as well as opportunities to contribute to wider training and outreach activities. Further training in both academic and interdisciplinary skills will be available as part of Edinburgh’s Institute for Academic Development. 

Prepare documentation required for conditional admission in the PhD programme. 

• Engineering PhD application requirements 

Please note that this requires a formal 2-page research proposal.

• How to write a proposal 

We welcome applications from all qualified candidates, and we wish to particularly encourage applications from groups underrepresented at this level. To apply to this opportunity, you will need to:

1. Meet entry requirements. Note this mainly relates to 

(a) have a degree classification of at least 2:1 or equivalent, 

(b) have funding (deadline end of January) or plans to apply to our scholarship programme (deadline January 12th 2026), 

(c) meet English requirements. Further information on English language requirements for EU/Overseas applicants.

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.

Further information and other funding options.

Dr. Jasotharan Sriharan is a Research Associate in Composite Design and Testing at The University of Edinburgh and a member of the MATTERS Group. His research focuses on developing advanced design tools to accelerate material and structural innovation.

He is experienced in the design, manufacturing, and testing of architected materials structures. His current research interests include:

• Inverse design of architected materials and structures
• Local buckling of thin-walled composite structures
• Multifunctional cellular materials
• Mechanics of advanced cellular and sandwich structures
• Additive manufacturing techniques for advanced materials