Publisher
source

Dongfang Liang

Top university

Just added

1 day ago

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

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Apr 15, 2026

Country flag

Country

United Kingdom

University

University of Cambridge

Social connections

How do I apply for this?

Sign in for free to reveal details, requirements, and source links.

Continue in dashboard

Where to contact

Official Email

Keywords

Computer Science
Environmental Science
Meta-analysis
Hydrology
Civil Engineering
Wastewater Treatment
Big Data
Green Infrastructure
Statistics

About this position

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

The University of Cambridge is offering a fully-funded PhD studentship through the EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Unlocking Net Zero (FIBE3 CDT), in partnership with Ward & Burke, a leading engineering firm in water and wastewater infrastructure. This four-year (1+3 MRes/PhD) programme focuses on the development of advanced AI tools for meta-analysis of hydraulic models, aiming to prevent combined storm overflows and improve sewage network performance.

The project addresses a critical gap in the global understanding and quantitative assessment of hydraulic models used for designing sewage network upgrades. Currently, model outputs are highly dependent on individual modellers, their assumptions, and model setups, leading to uncertainty in decision-making and scheme design. The research will develop computationally efficient meta-analysis techniques, leveraging AI and big data, to rank models, guide interventions, and assess the need for further optimisation. Key objectives include: developing a detailed understanding of current hydraulic modelling practices; creating a framework to quantitatively assess and rank catchment and sewage network models; providing guidance on sites and models that would benefit from further optimisation; and evaluating the cost, complexity, and embodied carbon of different intervention types. The project also explores blue/green and sustainable solutions to enhance network performance.

Applicants should hold, or expect to obtain, at least a high 2.1 degree (preferably at Masters level) in any STEM subject, with strong quantitative and analytical skills. The studentship covers full fees and maintenance for eligible home students, with limited funding available for international candidates. For project-specific enquiries, contact Professor Dongfang Liang at [email protected]. General enquiries can be directed to [email protected].

Applications should be submitted online via the University of Cambridge Applicant Portal, clearly stating the project title and Professor Dongfang Liang as supervisor. Early applications are encouraged, as offers may be made before the deadline of 15 April 2026. Please note a £20 application fee applies. The University of Cambridge is committed to equality, diversity, and inclusion, welcoming applicants from all backgrounds.

For further details on the programme, funding, and eligibility, visit the FIBE3 CDT website and the provided funding links. This opportunity is ideal for candidates interested in the intersection of civil engineering, environmental science, and AI-driven infrastructure optimisation.

Funding details

Available

What's required

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. Strong quantitative and analytical skills are preferred. Eligibility for full funding is primarily for home students; a limited number of international students may be considered for funding later. There is a £20 application fee. No specific language test requirements are mentioned, but standard University of Cambridge requirements apply.

How to apply

Apply online via the University of Cambridge Applicant Portal, stating the project title and Professor Dongfang Liang as supervisor. Early applications are encouraged as offers may be made before the deadline. Ensure you pay the £20 application fee.

Ask ApplyKite AI

Professors