AI-enhanced climate storylines for agricultural adaptation and resilience

About the Project

Introduction

This PhD project is part of the Cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia. The supervision team will be drawn from the two universities. This project will start at Coventry University, UK.

Agricultural systems are increasingly vulnerable to climate extremes such as droughts, floods and heatwaves, which are intensifying under climate change and threatening food production, land use and rural livelihoods. Yet, planning for such extremes remains a challenge because conventional climate projections often lack the spatial resolution, sectoral relevance, and narrative framing needed for effective decision-making. Climate storylines have therefore emerged as a powerful tool for communicating climate risk and uncertainty, offering physically consistent narratives of extreme events or climate pathways. However, generating meaningful climate storylines for agriculture is challenging. Conventional climate models struggle to capture extreme events at the spatial and temporal scales relevant to farming decisions, making it difficult to construct storylines that are both scientifically robust and practically useful for agricultural planning.

Project details

This exciting PhD project will develop a novel framework for generating AI-enhanced climate storylines tailored specifically to agricultural adaptation in the face of a changing climate. A talented and motivated doctoral candidate will have the opportunity to exploit recent advances in AI that present opportunities to enhance the development of storylines for agro-climatological futures. The approach will combine the latest developments in machine learning-based downscaling of the outputs of state-of-the-art global climate models alongside other AI applications to identify patterns of past and projected extremes. These data-driven storylines will be further refined through participatory co-production with agricultural stakeholders, ensuring they reflect local realities, vulnerabilities, and decision contexts. The framework will also explore the exciting potential for agentic AI to facilitate the autonomous exploration of high-resolution climate datasets to identify plausible projections of event types relevant to specific agricultural regions or stakeholder concerns.

This project will address the following key questions:

• How can AI-tools be used to generate high-resolution, stakeholder-relevant climate storylines for agriculture?
• What combinations of climate drivers and impacts are most important for agricultural planning?
• How can local knowledge and agricultural thresholds be integrated into climate storyline development?
• What role can agentic AI play in supporting the co-creation and interpretation of climate narratives?

Project supervision will be led by Associate Prof. Jonathan Eden (Coventry University) and Associate Prof. Robert Faggian (Deakin University), and supported by Prof. Matthew England (Coventry University) and Dr. Bahareh Nakisa (Deakin University).

Funding

Tuition fees and bursary

Benefits

The successful candidate will receive comprehensive research training including technical, personal and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral and Researcher College, which provides support with high-quality training and career development activities.

This is an exciting opportunity to study a PhD as part of a cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia. The PhD Student will graduate with two PhDs, one from Deakin University and one from Coventry University, each of which recognises that the program was carried out as part of a jointly supervised doctoral program.

Candidate specification

Applicants must meet the admission and scholarship criteria for both Coventry University and Deakin University for entry to the cotutelle programme.

• Applicants should have graduated within the top 15% of their undergraduate cohort. This might include a high 2:1 in a relevant discipline/subject area with a minimum 70% mark (80% for Australian graduates) in the project element or equivalent with a minimum 70% overall module average (80% for Australian graduates).
• A Bachelor's degree in a relevant field requiring at least four years of full-time study, and which normally includes a research component which is equivalent to at least 25% of a year’s full-time study in the fourth year, with achievement of a grade for the project equivalent to a H1 standard or 80%.

OR

• a Masters degree, with a significant research component, in a relevant subject area, with overall mark at minimum Distinction.
• In addition, the mark for the Masters thesis (or equivalent) must be a minimum of 80%.
• Please note that where a candidate has 70-79% and can provide evidence of research experience to meet equivalency to the minimum first-class honours equivalent (80%+) additional evidence can be submitted and may include independently peer-reviewed publications, research-related awards or prizes and/or professional reports.
• Language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component).

The potential to engage in innovative research and to complete the PhD within a prescribed period of study.

Additional Requirements

• Strong quantitative skills and experience in one or more of: statistical modelling, machine learning, climate data analysis, or environmental modelling.
• Experience using scientific programming languages such as Python or R (or willingness to develop these skills).
• An interest in climate change, agricultural systems, and climate risk communication.
• Ability to work across disciplines and engage with stakeholders (e.g., farmers, planners, policymakers) during coproduction activities.
• Good analytical skills and attention to detail when working with large and complex datasets.

How to apply

To find out more about the project, please contact ac6218@coventry.ac.uk

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.