Physics-Informed Machine Learning for Water Prediction

Accurate prediction of water systems, including groundwater dynamics, river flows, flooding, water quality and contaminant transport, is increasingly important under climate change, population growth and pressures on water resources. Conventional physics-based models provide mechanistic understanding but can be computationally expensive and constrained by uncertain parameters and incomplete observations, while purely data-driven machine-learning models can achieve high predictive accuracy but often lack physical consistency, interpretability and reliability beyond their training conditions. This PhD project will develop a new generation of Physics-Informed Machine Learning (PIML) approaches that integrate governing physical laws, observational data and modern artificial intelligence to provide accurate, computationally efficient and physically trustworthy predictions of water systems.<br /> <br /> The research will investigate advanced approaches including Physics-Informed Neural Networks (PINNs), neural operators and physics-guided deep learning, with particular emphasis on incorporating conservation laws, hydrological processes and uncertainty directly into AI architectures. Models will be developed and evaluated using real-world hydrological and environmental datasets for applications such as groundwater-level prediction, catchment-scale flow forecasting, flood and drought prediction, and contaminant transport. The project will investigate prediction across both space and time, including challenging long-term forecasting and data-sparse conditions, and benchmark the proposed methods against conventional numerical models and state-of-the-art machine-learning approaches. Ultimately, the research aims to establish a transferable AI-for-Water framework that combines the predictive power of machine learning with the reliability and interpretability of physical modelling, supporting more resilient and sustainable management of water resources under a changing climate.

<p>To apply for this project you will need to make a formal application for research degree study through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University website</a>. You will need to create a login ID with a username and PIN. </p> <p>•    For <strong>Application type</strong> please select <strong>Research Degrees – Research Postgraduate</strong>. <br /> •    The admission year for this project is <strong>2027/28</strong> Academic Year. <br /> •    You will need to select your <strong>Planned Course of Study</strong> from a drop-down menu. For this project, scroll down and select <strong>PHP Civil Engineering Full Time.</strong> <br /> •    The project start date for this project is <strong>1 October 2027</strong>, please use this as your <strong>Proposed Start Date of Research</strong>. <br /> •    Please state clearly in the<strong> Research Information Section</strong> that the research degree you wish to be considered for is <strong>Physics-Informed Machine Learning for Water Prediction</strong> as well as Dr <a href="https://eps.leeds.ac.uk/civil-engineering/staff/756/dr-xiaohui-chen">Xiaohui Chen</a> as your proposed supervisor.</p> <p><strong>Please state clearly in the Finance section</strong>, <strong>the funding that you are applying for, if you are self-funding or externally sponsored</strong>.</p> <p>More information on how to apply is available on our website <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">here</a>. You will be required to provide a personal statement which outlines your interest in the project you are applying for, why you have chosen it and how your skills map onto the requirements of the project.</p> <p>We will assess applications continuously as we receive them. We welcome and strongly encourage any potential applicants to contact the supervisor(s) for an informal discussion, prior to applying, and recommend submitting your application early.</p> <p><strong>If you are applying with external sponsorship or you are funding your own study, please ensure you provide your supporting documents at the point you submit your application:</strong></p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date including the grading scheme</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p> </p>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p>This is a non funded PhD project. Applicants are expected to be self funded or to secure external funding.</p> <p>You can find out more about our Funding Opportunities on our <a href="https://phd.leeds.ac.uk/">Postgraduate Research Opportunities</a> website.</p> <p><strong>Important: </strong>Please note that all costs associated with your arrival at Leeds (<a href="https://www.leeds.ac.uk/international-visas-immigration/doc/applying-student-visa">visa, Immigration Health Surcharge</a>, flights etc) would have to be met by yourself, or you will need to find an alternative funding source. </p>

<p>For further information about your application, please contact PGR Admissions by email to <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a></p> <p>For further information about this project, please contact Dr Xiaohui Chen by email to <a href="mailto:x.chen@leeds.ac.uk ">x.chen@leeds.ac.uk </a></p>