PhD Studentship: Explainable AI for Sustainable Sewers under Climate and Urban Stress

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).

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.

Funding

The QUEX Institute studentships are available for January 2025 entry.

This prestigious programme provides full tuition fees, stipend of £20780 p.a, travel funds of up to £15,000, and RTSG of £10,715 over the life of the studentship. 

The studentship funding is provided for up to 42 months (3.5 years)

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