"EPSRC FIBE3 CDT PhD studentship with Ward & Burke: Development of AI tools for meta-analysis of hydraulic models for preventing combined storm overflows"

This is a four-year (1+3 MRes/PhD) studentship funded through the Cambridge EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Unlocking Net Zero (FIBE3 CDT). Further details can be found at https://www.net-zero-fibe-cdt.eng.cam.ac.uk/.

The project is funded in collaboration with Ward & Burke, a leading engineering firm specialising in the design, manufacture, supply, installation, commissioning, operation and maintenance of water and wastewater infrastructure within Ireland and the UK.

Currently, there are no global scale views or quantitative performance metrics for hydraulic models to design sewage network upgrades. In order to reduce combined sewer overflows, tools are needed to optimise the interventions and compare different sites and models.

Presently, the outputs of each model are dependent on the modellers and assumptions they make, the model setup, and the "fudge factors" used. We do not yet have clarity about how these dependencies interact, especially as a third party, making it impossible to make informed decisions. This leads to scheme designs based on the model outputs without fully comprehending the quality of these outputs, whether they have been optimised sufficiently, or whether other options may be available.

This project will seek to develop methods to rank models and guide interventions based on whether further optimisation of the solution is worthwhile. The developed tools will not rely on computationally expensive modelling, but will use meta-analysis techniques to consider the scale/cost of the intervention, "quality" of the modelling, sensitivity of the model to further optimisation, etc.

The project objectives are to:

1. Develop a detailed understanding of current hydraulic modelling practice.
2. Create a new framework to quantitatively assess and rank different catchment hydraulic models and sewage network models, leveraging AI and other "big data" meta-analysis techniques to perform this in a computationally efficient manner.
3. Provide guidance as to which sites and models would continue to significantly benefit from further optimisation.
4. Assess cost, complexity, and embodied carbon of different types of intervention to provide a wholistic view of where further development should be targeted.
5. Evaluate the potential for blue/green and sustainable solutions for improvement to network performance.

For project-specific enquiries please e-mail Professor Dongfang Liang dl359@cam.ac.uk. For general enquiries, please email cdtcivil-courseadmin@eng.cam.ac.uk.

Applicants should have (or expect to obtain by the start date) at least a high 2.1 degree preferably at Masters level in any STEM subject.

Fully-funded studentships (fees and maintenance) are only available for eligible home students in the first instance. A limited number of international students can be considered for funding at a later stage in the recruitment process. Further details about eligibility and funding can be found at: https://www.ukri.org/councils/esrc/career-and-skills-development/funding-for-postgraduate-training/eligibility-for-studentship-funding/ https://www.postgraduate.study.cam.ac.uk/finance/fees https://www.cambridgetrust.org/scholarships/

Applications should be made online via the University of Cambridge Applicant Portal: https://www.postgraduate.study.cam.ac.uk/courses/directory/egegpdfib stating project Development of AI tools for meta-analysis of hydraulic models for preventing combined storm overflows with Prof. Dongfang Liang noted as supervisor. Please note there is a £20 application fee.

Applications will be reviewed soon after they are received, hence early applications are strongly encouraged as an offer may be made before the stated deadline.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

Key information

Department/location
Department of Engineering

Salary

Reference
NM48334

Category
Studentships

Date published
17 December 2025

Closing date
15 April 2026