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Dongfang Liang

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1 month ago

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

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Expired

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Country

United Kingdom

University

University of Cambridge

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Where to contact

Official Email

Keywords

Computer Science
Environmental Science
Meta-analysis
Hydrology
Artificial Intelligence
Civil Engineering
Wastewater Treatment
Environmental Sustainability
Big Data
Statistics

About this position

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

This fully-funded PhD studentship is offered through the Cambridge EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Unlocking Net Zero (FIBE3 CDT) at the University of Cambridge, in partnership with Ward & Burke, a leading engineering firm in water and wastewater infrastructure. The project aims to develop advanced AI tools for meta-analysis of hydraulic models, with the goal of preventing combined storm overflows and optimising sewage network upgrades.

Current challenges in hydraulic modelling include a lack of global performance metrics and the dependence of model outputs on individual modellers' assumptions and setup. This project will address these issues by creating a new framework to quantitatively assess and rank catchment hydraulic models and sewage network models. Leveraging artificial intelligence and big data meta-analysis techniques, the research will enable computationally efficient evaluation of model quality, sensitivity to optimisation, and the cost and complexity of interventions. The project will also consider embodied carbon and the potential for blue/green and sustainable solutions to improve network performance.

Key objectives include: developing a detailed understanding of current hydraulic modelling practices; designing a framework for quantitative assessment and ranking of models; providing guidance on which sites and models would benefit most from further optimisation; and evaluating the cost, complexity, and sustainability of different intervention strategies. The research will contribute to more informed decision-making in infrastructure design and environmental management, supporting the transition to net zero.

Applicants should hold, or expect to obtain, at least a high 2.1 degree (preferably at Masters level) in a STEM subject, with strong quantitative and computational skills. Fully-funded studentships (covering fees and maintenance) are available for eligible home students, with limited funding for international students considered at a later stage. The University of Cambridge actively supports equality, diversity, and inclusion, and encourages applications from all backgrounds.

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 recommended, as offers may be made before the deadline of 15 April 2025. For project-specific enquiries, contact Professor Dongfang Liang at [email protected]. For general enquiries, email [email protected].

Further details about the programme and funding can be found at the provided links. Please note there is a £20 application fee.

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. Preference is given to candidates with strong quantitative, analytical, and computational skills. Eligibility for full funding is primarily for home students; a limited number of international students may be considered for funding at a later stage. There is a £20 application fee. The University encourages applications from all backgrounds and supports equality, diversity, and inclusion.

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 meet eligibility criteria and prepare to pay a £20 application fee.

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