Human-in-the-Loop AI for Equitable and Climate-Resilient Sewer Systems. Department of Computer Science, UQ-Exeter Institute PhD Studentship (Funded) for January 2027 Entry Ref: 5845

Join a world-leading, cross-continental research team

The UQ Exeter Institute is seeking exceptional students to join a world-leading, international research partnership tackling major challenges facing the global community in sustainability and wellbeing. Our joint PhD program 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 at The University of Queensland and the University of Exeter. This prestigious program provides full tuition fees, stipend, travel and development funds and Research Training Support Grants to the successful applicants.

This select group of high-calibre doctoral candidates will have the chance to study in the UK and Australia, and will graduate with a joint PhD degree from The University of Queensland and the University of Exeter.

The studentship provides funding for up to 42 months (3.5 years).

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 four priority themes.

Successful applicants will undertake this joint program on a full-time and onshore basis, commencing in Australia (UQ-homed) or in the UK (Exeter-homed). At least 12 months will be spent at each institution over the period of the joint PhD program.

The closing date for applications is midday Friday, 24 April 2026 (BST), with interview to be held between Monday, 25th May and Wednesday, 3rd June 2026. 

The start date is expected to be Monday, January 4th January 2027.

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 – Dr Jawad Fayaz

UQ – Professor Steven Kenway

Project Description

Urban sewer and stormwater systems are increasingly stressed by climate change and rapid urbanisation. Intensifying rainfall, more frequent extreme events, and expanding impervious surfaces have increased sewer overflows that contaminate ecosystems, threaten public health, and breach environmental regulations. Infrastructure designed for historical rainfall regimes can no longer cope with the emerging “new normal”, where events once classified as rare now occur far more frequently. In England and Wales, sewer overflow impacts exceed £270 million annually and affect approximately 80,000 homes; in Australia, annual costs exceed AUD 982 million. These failures disproportionately affect vulnerable communities and require utilities to make timely, accountable intervention decisions under uncertainty.

Utilities must prioritise interventions such as pipe upgrades, storage expansion, real-time control, or maintenance under uncertain climate futures, constrained budgets, and regulatory obligations. Hydraulic simulators are physically detailed but computationally slow and calibration-intensive, limiting large-scale scenario exploration and optimisation. Purely data-driven approaches are faster but can produce opaque decisions and amplify inequities if trained on historically biased patterns or narrow objectives. There is therefore a clear methodological gap: the absence of a rapid, data-driven, physically informed and human-aligned decision framework capable of robust, equity-aware intervention planning under climate uncertainty.  

This PhD will develop a decision-support framework integrating physics-informed machine learning, scenario generation, and human-in-the-loop preference-based reinforcement learning to prioritise climate-robust and equity-aligned interventions. The core innovation is embedding expert judgement, regulatory constraints, and equity objectives directly into policy learning using structured preference feedback and explicit constraints.  

The framework has three coupled components. First, a physics-informed graph surrogate model will emulate network hydraulics at scale, representing pipes and assets as a graph and predicting flows, depths, surcharge conditions, and overflow likelihood under rainfall and operational boundary conditions. Second, scenario generation will create climate and urban growth stress-tests by downscaling projections and integrating plausible land-use changes, with uncertainty explicitly represented to evaluate robustness rather than single-point forecasts. Third, a preference-learning reinforcement learning agent will propose intervention portfolios. Stakeholder preference rankings derived through inverse reinforcement learning and constraints related to compliance, safety, cost, and equity will guide policy learning, ensuring auditable and human-aligned decisions. 

Deliverables include validated surrogate models for rapid risk evaluation, scenario libraries for stress-testing, a human-in-the-loop optimisation engine for invertion decisions with auditable decision logs, and reusable software modules suitable for utility integration. 

Research objectives   
Build and validate physics-informed graph surrogate models for sewer network states and overflow outcomes. Develop climate and urban growth scenario generation with uncertainty. Implement preference-based reinforcement learning with explicit human and regulatory constraints. Evaluate policies on UK and Australian case studies using performance and equity metrics.  

Key research questions  

• How can stakeholder preferences and regulatory constraints be encoded to produce auditable, equity-aligned policies?
• How does climate and urban uncertainty alter robust intervention prioritisation?
• What performance and equity gains are achievable relative to hydraulic-only and purely data-driven baselines?   

This project contributes directly to the Global Environmental Futures agenda by advancing methods for managing critical urban infrastructure under accelerating climate uncertainty while explicitly accounting for social and environmental equity. By delivering a validated, human-in-the-loop decision-support framework tested on real sewer networks in the UK and Australia, the research demonstrates how climate adaptation decisions can be made robust, transparent, and accountable rather than reactive or opaque. The resulting framework is designed to be transferable across regions and regulatory contexts, supporting long-term resiliency and sustainability of climate-adaptive cities under the Global Environmental Futures theme.

Contact

Questions about this project should be directed to Dr Jawad Fayaz at J.Fayaz@exeter.ac.uk

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

To apply for this studentship project please use the 'Apply now' button above.  Important note:  If you apply for this project via a different route your application will not be considered. 

You will be asked to submit some personal details and upload the following documents:

• a full CV
• A Personal Statement.  Please use the following form UQ-Exeter Institute Personal Statement,   Please note: the document will open as read only so please ensure you save a copy onto your desktop to edit the document.  Please ensure you upload the completed document to your application.
• academic transcripts and degree certificates
• details of two academic referees.
• English Language qualification.

Please quote reference 5845 on your application and in any correspondence about this studentship.

Summary