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Jawad Fayaz

7 months ago

PhD in Human-in-the-Loop AI for Climate-Resilient Sewer Systems (Machine Learning, Environmental Engineering) University of Exeter in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

The position is fully funded for up to 3.5 years, covering full international or national tuition fees, an annual stipend of £21,805, a travel budget of up to £15,000, and a Research Training Support Grant (RTSG) of £10,715. Funding supports study in both the UK and Australia.

Deadline

Expired

Country flag

Country

United Kingdom

University

University of Exeter

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Keywords

Computer Science
Data Science
Environmental Science
Civil Engineering
Reinforcement Learning
Equity
Statistics
Applied Mathematic

About this position

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.

Funding details

The position is fully funded for up to 3.5 years, covering full international or national tuition fees, an annual stipend of £21,805, a travel budget of up to £15,000, and a Research Training Support Grant (RTSG) of £10,715. Funding supports study in both the UK and Australia.

What's required

Applicants must have, or expect to obtain, a first or upper-second class BA or BSc (or equivalent) in a relevant discipline such as Computer Science, Engineering, Data Science, Statistics, or Applied Mathematics. Strong computational skills are required. If English is not the applicant's first language, proof of English proficiency is required via an accepted language test. Applicants should be highly motivated with a strong academic background and track record.

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Professors

Professor

University Name
Country flag

United Kingdom

Research Interests

Statistics

Reinforcement Learning

Computer Science

Civil Engineering

Equity

Professor, FICE, CEng

University Name
Country flag

United Kingdom

Research Interests

Geotechnical Engineering

Soil Science

Geomechanics

Soil Chemistry

Cement Chemistry

Professor

University Name
Country flag

United Kingdom

Research Interests

Statistics

Artificial Intelligence

Computer Science

Air Traffic Management

Reinforcement Learning