PhD in Human-in-the-Loop AI for Climate-Resilient Sewer Systems (Machine Learning, Environmental Engineering)
The University of Exeter, in partnership with the University of Queensland, is offering a fully funded joint PhD position focused on 'Human-in-the-Loop AI for Equitable and Climate-Resilient Sewer Systems.' This interdisciplinary project aims to develop next-generation AI systems that are robust, interpretable, and aligned with human decision-making for climate-critical urban infrastructure. The research will integrate physics-informed machine learning, scenario generation under uncertainty, and reinforcement learning with human feedback to address real-world challenges in sewer and stormwater systems affected by climate change and urbanization.
Key research areas include the development of graph surrogate models for network hydraulics, climate and urban growth scenario generation, and preference-based reinforcement learning that incorporates stakeholder and regulatory constraints. The project is part of the Global Environmental Futures theme and will be supervised by an international team including Dr Jawad Fayaz (University of Exeter), Professor Steven Kenway (University of Queensland), and others. Students will benefit from access to world-class research groups, interdisciplinary supervision, industry alignment, and international collaboration, with at least 12 months spent at each institution.
Applicants should have a strong background in Computer Science, Machine Learning, Data Science, Statistics, Applied Mathematics, or Engineering with computational skills. The position is fully funded for 3.5 years, covering tuition, a £21,805 annual stipend, £15,000 travel budget, and £10,715 Research Training Support Grant. The application deadline is 24 April 2026, and the program is expected to start in January 2027. For more information and to apply, visit the University of Exeter funding page and quote reference 5845.
For questions, contact Dr Jawad Fayaz at [email protected]. This opportunity is ideal for highly motivated students interested in AI, environmental engineering, and climate adaptation research.
