Hybrid Machine Learning–Integrated Modelling of Global Agricultural Systems Imperial College London in United Kingdom

This fully funded PhD studentship at Imperial College London offers an exciting opportunity to join the Water Systems Integration Group in the Department of Civil and Environmental Engineering. The project, supervised by Professor Ana Mijic and co-supervised by Dr Jimmy O’Keeffe (Dublin City University), is part of an international research consortium focused on enabling local knowledge production for water security. Collaborations extend to Dr Rossella Arcucci (Imperial College London), Dr Barnaby Dobson (Trinity College Dublin), and Dr Seifu Tilahun (International Water Management Institute).

The research addresses the complexity and dynamism of agricultural systems, which integrate biophysical processes such as soil, water, and crops with human decision-making and institutional frameworks. These systems are vital for human livelihoods but also pose significant environmental challenges, particularly regarding water use and agrochemical runoff. The project aims to develop advanced integrated computational models that capture key interactions across multiple spatial and temporal scales, providing new insights into agriculture's role within the broader human–water cycle.

This interdisciplinary PhD will focus on improving the representation of agricultural systems within flexible integrated water system models. Unlike traditional detailed modelling approaches, the project will create a reduced complexity representation of agricultural systems, embedded within integrated water system models and enhanced through machine learning (ML) enabled data assimilation. The research will test these simulations for consistency of agricultural impacts and policy relevance in diverse settings, leveraging a global network of collaborators.

You will work with WSIMOD, a state-of-the-art integrated water system model, extending it by developing a globally generalisable agricultural systems module capable of representing key global crops such as wheat, rice, maize, soybean, and potato. The project will explore hybrid WSIMOD–ML approaches, using ML-based data assimilation techniques to support model parameterisation, evaluation, and uncertainty reduction. The PhD is embedded in a larger international consortium project for global water assessment, with the results supporting evidence-based decision-making on water security and agricultural productivity.

The ideal candidate will have a strong interest in machine learning and data assimilation approaches, experience in agricultural systems modelling, and quantitative data analysis. Applicants should hold a First Class Degree (or international equivalent) in water engineering, environmental engineering, or a closely related discipline with a strong quantitative or data-analysis component, as well as a Masters level degree qualification. Experience with modelling and programming, ideally using Python, for water systems analysis is required. Enthusiasm for collaborative and interdisciplinary research, along with excellent English communication skills, is essential.

The studentship provides funding for 4 years from the start date (1 October 2026), covering international tuition fees and a tax-free stipend at the standard UKRI London rate. Applications are accepted year-round and will be reviewed until the position is filled.

To apply, contact Professor Ana Mijic ([email protected]) for further details and informal discussions. Submit your current CV, a covering letter (maximum 1 page), and contact details of two academic referees by email, using the subject line 'PhD Application: Hybrid Machine Learning–Integrated Modelling of Global Agricultural Systems'. Application via the Imperial College Registry is not required at this stage.

For more information, visit the project page: Hybrid Machine Learning–Integrated Modelling of Global Agricultural Systems.