[School of Natural Sciences PhD Scholarships] Advancing the Joint UK Land Environment Simulator (JULES) through Data Science and Emulation

About the project

About the Project How can we make urban climate modelling faster and more accessible? This PhD project will use environmental data science to advance the Joint UK Land Environment Simulator (JULES), a core component of the UK’s national weather, climate and environmental modelling infrastructure. You will develop a fast data science emulator of urban land-surface processes, exploring how cities store and exchange heat and how interventions such as reflective surfaces and vegetation could influence urban thermal conditions. The research will assess accuracy, physical consistency and reliability during extreme heat events. You will also develop a natural-language interface that allows users to configure simulations, compare scenarios and interpret results. Supervised by Dr Zhonghua Zheng and Professor David Topping at The University of Manchester, you will receive training in urban climate science, land-surface modelling, data science, scientific computing and interactive application development. The project offers an opportunity to combine methodological research with practical tools for urban climate adaptation. We welcome applicants with backgrounds in environmental science, meteorology, geography, engineering, mathematics, computer science or related disciplines, and an interest in programming and environmental challenges. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Natural Sciences Scholarships . If you don’t already have an applicant account, please follow the instructions here . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing your motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in Environmental Science, Atmospheric Science, Geography, Engineering, Computer Science, Mathematics, Physics, or a related discipline (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline Environmental Science, Atmospheric Science, Geography, Engineering, Computer Science, Mathematics, Physics, or a related discipline (or international equivalent). Previous research experience in Programming and Quantitative analysis is desirable. First-author publication experience in a peer-reviewed journal is particularly desirable. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoNS

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