PhD: Using Statistical Emulators to Quantify and Understand Urban Climate Risks

Using Statistical Emulators to Quantify and Understand Urban Climate Risks UNRISK NERC Centre for Doctoral Training PhD studentship 2026/27 Entry. Ref: 5714

About the award

Supervisors

Primary Supervisor

Dr Hossein Mohammadi

Institution

University of Exeter (Department of Mathematics and Statistics)

Academic Supervisors

Professor Jennifer Catto

Understanding Uncertainty to Reduce Climate Risks (UNRISK) is a Centre for Doctoral Training – Recruiting now!

UNRISK is a Centre for Doctoral Training with fully funded PhD research opportunities at the University of Leeds, University College London, and the University of Exeter collaborating with over 40 external partners. UNRISK will train students with the multidisciplinary knowledge and skills across climate science, data science and decision science to tackle the pressing challenge of reducing the risks associated with rapid climate change. UNRISK will fund 40 PhD students in cohorts of 12-15 per year over three years, providing a stipend, university fees and residential training for 3 years and 9 months. Find out more at https://unrisk-cdt.ac.uk/ and browse the projects at https://unrisk-cdt.ac.uk/projects/.

Project Information

Background

As climate change intensifies, cities face growing risks from extreme weather events such as heatwaves and floods. Addressing these risks requires simulation models to forecast various scenarios. However, these models are often computationally intensive, which limits their use in analyses such as uncertainty quantification, where many simulation runs are needed. To overcome this issue, statistical emulators are used to approximate complex models with far lower computational costs. Emulators allow for faster analysis and a deeper understanding of uncertainty and climate risks.

Despite recent advances, important challenges remain: for example, how can we ensure the accuracy and interpretability of emulators when they are used to assess climate risks such as flooding? Advancing research in this area is critical to building more resilient urban systems and offers a valuable opportunity for STEM graduates to contribute to environmental solutions through innovative, data-driven methods.

PhD opportunity

Climate change is a critical global challenge, demanding urgent policy responses to manage risks such as flooding and extreme heat. High-fidelity simulation models are essential for studying the impacts of different climate scenarios. However, their substantial computational cost restricts their use in tasks such as scenario exploration and rigorous uncertainty quantification (UQ), both of which require many simulation runs.

This PhD project aims to address these challenges by developing advanced data-driven methods to quantify and interpret uncertainties in multi-fidelity climate simulators. The focus will be on statistical emulators, e.g. Gaussian processes, combined with adaptive design of experiments that automatically select new simulation runs to maximise information gain across different fidelity levels. This approach will enable efficient approximations of high-fidelity models and help overcome computational barriers in exploring climate scenarios and quantifying uncertainty.

Working at the intersection of statistics, machine learning, and climate science, the student will develop emulators to model urban impacts of climate extremes. The project will also investigate the limitations of emulators, including high dimensionality and extrapolation issues. Key research questions include how different heatwave and flood scenarios influence infrastructure resilience and population vulnerability in cities. To address these, the PhD will advance UQ methods and explainable AI tools tailored to support decision-making.

The PhD will offer interdisciplinary supervision and training, including collaboration with Dr Saves’ team at Université Toulouse Capitole in France. Their expertise in urban mobility, flash flooding, spatial planning, and climate risk modelling will strengthen the scientific underpinnings of the project and enhance its practical relevance across European urban environments. The project will also be co-supervised by Professor Jennifer Catto at Exeter.

Applicant Profile

We welcome applications from STEM graduates, particularly those with backgrounds in statistics, computer science, modelling, and optimisation, who are eager to address real-world challenges in climate change, sustainability, and urban decision-making. Prior exposure to interdisciplinary fields such as social sciences, urban modelling, or ecology is advantageous but not required. This PhD is ideal for candidates who are curious, highly motivated, and keen to bridge computational methods with pressing societal and environmental issues.

Key skills and interests:
• Proficiency in scientific computing and programming (Python, R or equivalent)
• Foundations in optimisation, statistical learning and machine learning
• A strong interest in environmental engineering, agent‐based systems and interdisciplinary science

Other information

• E. Cueille, D. Bodini, B. Gaudou, D. Grancher, P. Nicolle, O. Payrastre, M. Prédhumeau, I. Ruin, G. Terti, and N. Verstaevel. “”Assessing the impact of crisis cell decisions during flash flood””, In : International Workshop on Multi-Agent-Based Simulation, 2025
• H. Mohammadi and P. Challenor, “”Sequential adaptive design for emulating costly computer codes””, Journal of Statistical Computation and Simulation, 2025
• Manon Prédhumeau, “”Sustainable urban digital twins: Reducing, reusing, recycling models””, In: The 19th International Conference on Computational Urban Planning and Urban Management, 2025.

Funding

UNRISK will fund 40 PhD students in cohorts of 12-15 per year over three years, providing them tuition fees and a stipend for a period of 3 years 9 months which includes the expectation that PhD students will undertake a placement and also take part in cohort based residential research events. Further up to date information about studentship funding is available from UK Research and Innovation.

Applications are open to UK and international applicants, although the number of awards for international applicants is limited by UKRI rules. Please note that the grant cannot cover visa and NHS International Health Surcharge (IHS) costs, which are in the order of £3000-£4000 to allow overseas students entry to study in the UK.

Some additional places are also available for students who have their own funding, such as scholarships, and whose research is closely aligned with UNRISK.  

Part-time study can be offered to students unable to join the programme as a full-time student. Please get in touch with us if you would like to discuss this option. 

If you have any questions about the application process please email ENV-PGR@leeds.ac.uk.

Entry requirements

Academic Entry Requirements

You should hold a first or upper-second class Bachelor’s degree or a taught Master’s degree in an appropriate subject from a UK university. Non-UK qualifications of an equivalent standard are also accepted.

Residency

The UNRISK CDT studentships are available to UK and International applicants. Following Brexit, the UKRI now classifies EU students as international unless they have rights under the EU Settlement Scheme. The GW4 partners have agreed to cover the difference in costs between home and international tuition fees. This means that international candidates will not be expected to cover this cost and will be fully funded but need to be aware that they will be required to cover the cost of their student visa, healthcare surcharge and other costs of moving to the UK to do a PhD. All studentships will be competitively awarded and there is a limit to the number of International students that we can accept into our programme (up to 30% cap across our partners per annum).
 

English Language Requirements

If English is not your first language you will need to meet the English language requirements by the start of the PhD programme. This will be at least 6.5 in IELTS or an acceptable equivalent. Please refer to the relevant university for further information. Please refer to the English Language requirements web page for further information.

How to apply

You must apply for funding via the University of Leeds website further details can be found here Application Process – Understanding Uncertainty to Reduce Climate Risks

Please note that only those applications submitted directly via the University of Leeds application system will be assessed for funding. Applying directly to your chosen programme of study at the University of Exeter will not constitute an application for funding.

Data Protection

If you are applying for a place on a collaborative programme of doctoral training provided by University of Leeds and other universities, research organisations and/or partners please be aware that your personal data will be used and disclosed for the purposes set out below.

Your personal data will always be processed in accordance with the General Data Protection Regulations of 2018. University of Leeds (“University”) will remain a data controller for the personal data it holds, and other universities, research organisations and/or partners (“HEIs”) may also become data controllers for the relevant personal data they receive as a result of their participation in the collaborative programme of doctoral training (“Programme”).

Further Information

For an overview of the UNRISK NERC CDT programme please see the website https://unrisk-cdt.ac.uk/

Summary