QUEX January 2026 Entry - Explainable AI for sustainable sewers under climate and urban stress. [Computer Science], PhD Studentship (Funded) Ref: 5530

Join a world-leading, cross-continental research team

The University of Exeter and the University of Queensland are seeking exceptional students to join a world-leading, cross-continental research team tackling major challenges facing the world’s population in global sustainability and wellbeing as part of the QUEX Institute. The joint PhD programme provides a fantastic opportunity for the most talented doctoral students to work closely with world-class research groups and benefit from the combined expertise and facilities offered at the two institutions, with a lead supervisor within each university. This prestigious programme provides full tuition fees, stipend, travel funds and research training support grants to the successful applicants.  The studentship provides funding for up to 42 months (3.5 years).

Eight generous, fully-funded studentships are available for the best applicants, four offered by the University of Exeter and four by the University of Queensland. This select group will spend at least one year at each University and will graduate with a joint degree from the University of Exeter and the University of Queensland.

Find out more about the PhD studentships www.exeter.ac.uk/quex/phds

Successful applicants will have a strong academic background and track record to undertake research projects based in one of the three themes of:  Healthy Living, Global Environmental Futures and Digital Worlds and Disruptive Technologies.

The closing date for applications is mid-day May 15th 2025 (BST), with interview to be w/c 16th June 2025 (tbc). The start date is expected to be Monday January 5th 2026.

Please note that of the eight Exeter led projects advertised, we expect that up to four studentships will be awarded to Exeter based students.

Theme:  Global Environmental Futures

Supervisors:

Exeter Academic Lead: Dr Jawad Fayaz

Queensland Academic Lead: Professor Steven Kenway

Project Description

Urban sewer and stormwater systems face escalating failures due to climate extremes and urbanisation. Ageing infrastructure, designed for historical rainfall patterns, now struggles with frequent “1-in-100-year” storms and urban sprawl, which increase toxic overflows, breach environmental regulations, and disproportionately harm marginalised communities. Traditional models like SWMM are computationally slow and lack scalability, while opaque AI methods risk biased outcomes. This project addresses these gaps by developing a responsible machine-learning framework that integrates climate resilience, equity, and cost-effectiveness into infrastructure management, aligning with UN SDGs 6 (clean-water) and 11 (sustainable-cities).

Objectives
Develop physics-based ML models to simulate sewer networks as dynamic systems, targeting ≥90% modelling accuracy.
Train an explainable decision-making agent to optimize interventions (e.g., pipe upgrades), balancing cost, equity, and compliance.
Resolve data harmonization challenges across utility systems to ensure tool functionality.
Validate outcomes through case studies in Brisbane and Exeter, targeting ≥20% overflow reduction.
Deliver open-source tools and training modules for global utility adoption.
The framework combines physics-informed graph-neural-networks (GNNs), diffusion model, and explainable reinforcement learning (XRL) to simulate sewer/stormwater system behaviour, predict risks, and optimize interventions. GNNs act as surrogate digital twins, embedding hydraulic principles to model how land-use changes and extreme weather impact flows. Nodes (junctions, tanks) and edges (pipes) encode hydraulic and climate data, predicting vulnerabilities like overflows.

A generative AI diffusion model synthesizes high-resolution climate-urban scenarios by downscaling global climate maps (e.g., CMIP6) and integrating urban growth projections. Combined with Bayesian uncertainty analysis, the simulated scenarios are used alongwith GNNs to identify overflow hotspots and pressure deficits.

A multi-objective explainable reinforcement learning (XRL) engine then optimizes interventions against environmental, financial, regulatory, and equity goals. Explainability tools—saliency maps, counterfactual analyses, and GNNExplainer—quantify trade-offs and clarify how actions reduce risks, building trust, ensuring regulatory compliance, and minimizing service disparities. Auditable decision trails mitigate bias.

DevOps/API pipelines automate deployment, while interactive dashboards visualize risks, policy impacts, and intervention outcomes. This end-to-end approach balances technical precision with transparency, enabling utilities to preempt failures and prioritize equitable, low-carbon solutions.

Institutional Expertise:
Exeter: Dr. Fayaz (physics-informed ML) and Prof. Javadi (hydraulic modelling) advance AI development using IDSAI’s GPU clusters, ISCA HPC, and Southwest Water’s utility datasets. Collaboration with HRWallingford enhances industry adoption and testing.
UQ: Prof. Kenway (urban water systems) and Dr. Moravej (water networks) provide SWMM integration and field validation via ACWEB, utilizing Urban Utilities data. Dr. Gibbes (hydroinformatics) guides scenario generation and policy formulations.
Collaboration: Joint workshops align tools with utility needs. Exeter develops the ML architecture; UQ validates models and deploys case studies.
Collaboration Phases:
Months 1–18 (Exeter): Data collation, GNN/diffusion model development.
Months 18–30 (UQ): XRL policy optimisation and historical validation.
Months 30–36 (Both): Case study deployment in Brisbane/Exeter, open-source release.
Months 36–42 (Exeter): Thesis completion, digital twin deployment.
Deliverables:
Python code for a modular framework integrating physics-informed GNNs, diffusion model, and XRL agent, with DevOps/APIs for utility integration.
Report analysing overflow reduction, cost savings, and equity improvements.
Dashboard visualizing overflow hotspots and intervention impact.
>2 peer-reviewed publications (e.g., Water Research) and training modules.

Entry requirements

Applicants should be highly motivated and have, or expect to obtain, either a first or upper-second class BA or BSc (or equivalent) in a relevant discipline.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information and a list of acceptable alternative tests.

How to apply

Apply now

You will be asked to submit some personal details and upload a full CV, personal statement, academic transcripts and details of two academic referees. Your supporting statement should outline your academic interests, prior research experience and reasons for wishing to undertake this project, with particular reference to the collaborative nature of the partnership with the University of Queensland, and how this will enhance your training and research.

Please quote reference 5530 on your application and in any correspondence about this studentship.

Summary