EPSRC FIBE3 CDT PhD Studentship: AI Tools for Meta-analysis of Hydraulic Models to Prevent Combined Storm Overflows University of Cambridge in United Kingdom

[Fully-funded studentships (fees and maintenance) for eligible home students; limited funding for international students may be available later.]

This EPSRC FIBE3 CDT PhD studentship at the University of Cambridge offers a unique opportunity to develop advanced AI tools for meta-analysis of hydraulic models, aiming to prevent combined storm overflows and optimise sewage network upgrades. The project is a collaboration with Ward & Burke, a leading engineering firm specialising in water and wastewater infrastructure across Ireland and the UK. The research addresses the lack of global quantitative performance metrics for hydraulic models, which are crucial for informed decision-making in sewage network design and intervention.

Over four years (1+3 MRes/PhD), you will investigate current hydraulic modelling practices, create frameworks to assess and rank catchment and sewage network models, and leverage AI and big data meta-analysis techniques for computational efficiency. The project will guide interventions by evaluating the quality and sensitivity of models, cost, complexity, and embodied carbon of various solutions, and explore blue/green and sustainable approaches to network performance improvement.

Funding is fully available for eligible home students, covering fees and maintenance. International students may be considered for funding at a later stage. Applicants should hold, or expect to obtain, at least a high 2.1 degree, preferably at Masters level, in any STEM subject. The University of Cambridge actively supports equality, diversity, and inclusion, encouraging applications from all backgrounds.

Applications are submitted online via the University of Cambridge Applicant Portal, with Professor Dongfang Liang as supervisor. Early applications are recommended, as offers may be made before the official deadline of 15 April 2026. For project-specific queries, contact Professor Dongfang Liang at [email protected]. For general enquiries, email [email protected]. Further information about eligibility and funding can be found on the provided links.