Advancing Machine Learning for Extreme Wind Speed Prediction, Engineering – PhD Ref: 5470

Supervisors

Dr Zhou Zhou, Lecturer in Data-Centric Engineering, University of Exeter

Prof. Prathyush Menon, University of Exetermenon

The University of Exeter’s is inviting applications for a fully funded PhD studentship to commence on on September 2025 (or sooner if appropriate).  For eligible students the studentship will cover Home or International tuition fees plus an annual tax-free stipend of at least £19,237 for 3.5 years full-time, or pro rata for part-time study. The student would be based in Engineering (but work closely with Computer Science) in the Faculty of Environment, Science and Economy at the Streatham Campus in Exeter.

Climate change is amplifying the frequency and intensity of extreme weather events, creating significant risks for infrastructure, economies, and communities. It highlights the urgent need for innovative methods to accurately forecast extreme weather events and reduce their impacts. Nevertheless, the unpredictable and infrequent nature of extreme weather events poses distinct challenges, requiring the development of advanced, data-driven solutions that can handle their complexity and rarity. 

This PhD project aims to pioneer ML methodologies tailored to short-term spatiotemporal extreme wind speed prediction, addressing limitations in conventional models. 

The candidate will: 

• Analyse Extreme Data Distributions: Apply advanced statistical techniques, such as extreme value theory, to understand and model rare high-impact wind events. 

• Innovate Model Architectures: Develop novel model structures such as spatiotemporal graph neural networks, physics-informed neural networks, and Bayesian approaches to address evolving weather patterns in both spatial and temporal dimensions. 

• Enhance ML Predictability: Maintain model predictability under data distribution shifts caused by extreme wind events, exploring strategies such as data augmentation and novel loss functions. 

• Collaborate and Validate: Work with experts from the Met Office to validate models using real-time meteorological data, ensuring actionable and reliable forecasting tools. As part of this transformative project, the PhD candidate will: 

• Contribute to addressing one of the most urgent challenges posed by climate change. 

• Work with the Met Office who will provide expertise, data and links to important stakeholders to support this project. 

• Work under a multidisciplinary supervision team. 

• Develop expertise in advanced ML techniques with real-world applications, positioning themselves as a leader in climate-resilient technologies. 

Entry requirements

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK. A strong foundation in machine learning and data science is essential, while knowledge of spatiotemporal modelling, numerical methods, or meteorology would be advantageous. Ideal candidates should possess excellent programming skills, critical thinking abilities, and a passion for tackling global challenges through innovative research.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information.

How to apply

Apply now

To apply, please click the ‘Apply Now’ button above. In the application process you will be asked to upload several documents

·       Cover letter (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).

·       CV

·       Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)

·       Two references from referees familiar with your academic work. If your referees prefer, they can email the reference direct to PGRApplicants@exeter.ac.uk quoting the studentship reference number.

·       If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English (Before any unconditional offer is made).

The closing date for applications is midnight on 7th April 2025.  Interviews will be held virtually in the week commencing TBC

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

Please quote reference  5470 on your application and in any correspondence about this studentship

For general information about this studentship and the application process, please contact PGRApplicants@exeter.ac.uk. Project specific queries should be directed to the main supervisor.

Summary