University College London
2 weeks ago
PhD in Robust and Scalable Spatio-Temporal Modelling for Climate Science University College London in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Competition-funded PhD project under the UNRISK CDT. Offers 15-18 fully funded NERC studentships covering full university tuition fees, a personal stipend at standard UKRI rates, £6000 individual research and training costs, £5000 per student of cohort-level training, and a Flexible Fund for special projects. International applicants may need to cover student visa costs and the international health surcharge; awards for international applicants are limited by UKRI rules.
Deadline
Jan 13, 2027
Country
United Kingdom
University
University College London

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About this position
PhD opportunity at University College London in Robust and Scalable Spatio-Temporal Modelling, hosted by the Department of Statistical Science and linked to the Fundamentals of Statistical Machine Learning research group.
This project sits at the interface of Statistics, Computer Science, Mathematics, Environmental Science, and Earth Science. The research aims to develop new statistical and machine-learning methodology for climate prediction that is more robust to imperfect observations and modelling assumptions. Topics include probabilistic machine learning, Gaussian processes, Kalman filtering, generalised Bayesian inference, robust statistics, spatio-temporal modelling, and scalable algorithms for large climate datasets.
The application focus is on satellite observations of the cryosphere and polar climate system, with collaboration with climate scientists and co-supervision by Prof. Michel Tsamados (UCL Earth Sciences). The project combines mathematical theory with practical algorithm development and evaluation, with attention to prediction quality, uncertainty quantification, and computational cost.
Funding is provided through the UNRISK CDT as a competition-funded PhD project. It offers 15-18 fully funded NERC studentships, including full university tuition fees, a stipend at standard UKRI rates, £6000 for individual research and training costs, £5000 per student for cohort-level training, and a Flexible Fund for special projects. International applicants are welcome, though they may need to cover visa and health surcharge costs and awards are limited by UKRI rules.
Applicants should have a strong mathematical background and strong programming skills. Suitable backgrounds include mathematics, statistics, computer science, or related disciplines. Prior research experience in statistics or machine learning is preferred, and industry experience is also welcomed. A strong interest in climate and environmental applications is essential, but prior background in these areas is not required.
The project is based in London, United Kingdom. For more information, see the UNRISK website and the project page.
Funding details
Competition-funded PhD project under the UNRISK CDT. Offers 15-18 fully funded NERC studentships covering full university tuition fees, a personal stipend at standard UKRI rates, £6000 individual research and training costs, £5000 per student of cohort-level training, and a Flexible Fund for special projects. International applicants may need to cover student visa costs and the international health surcharge; awards for international applicants are limited by UKRI rules.
What's required
Applicants should have a strong mathematical background and strong programming skills. Suitable backgrounds include undergraduate or postgraduate degrees in mathematics, statistics, computer science, or a related discipline. Previous research experience in statistics or machine learning is preferred, such as a research thesis or summer research placement. Relevant industry experience is also welcomed. A strong interest in climate and environmental applications is essential, though prior background in these areas is not required.
How to apply
Visit the UNRISK website for more information and apply through the PhD project listing. Review the project details, funding notes, and eligibility information before submitting your application.
More information can be found here
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