Robert Faggian

This cotutelle PhD project, jointly offered by Coventry University (UK) and Deakin University (Australia), focuses on developing AI-enhanced climate storylines to support agricultural adaptation and resilience in the face of climate change. The project addresses the increasing vulnerability of agricultural systems to climate extremes such as droughts, floods, and heatwaves, which threaten food production and rural livelihoods. Traditional climate projections often lack the resolution and narrative context needed for effective agricultural planning, making this research both timely and impactful. The doctoral candidate will develop a novel framework that leverages recent advances in artificial intelligence and machine learning to generate high-resolution, stakeholder-relevant climate storylines. The approach includes machine learning-based downscaling of global climate model outputs, identification of patterns in past and projected climate extremes, and participatory co-production with agricultural stakeholders to ensure the storylines are locally relevant and actionable. The project will also explore the use of agentic AI for autonomous exploration of climate datasets, aiming to identify plausible projections of event types critical to specific agricultural regions. Supervision is provided by a cross-institutional team: Associate Prof. Jonathan Eden (Coventry University), Associate Prof. Robert Faggian (Deakin University), Prof. Matthew England (Coventry University), and Dr. Bahareh Nakisa (Deakin University). The successful candidate will benefit from comprehensive research training, technical and professional development, and the unique opportunity to graduate with two PhDs—one from each institution—recognizing the joint nature of the program. Applicants must meet the entry and scholarship requirements of both universities, including a strong academic record (top 15% of undergraduate cohort or equivalent), significant research experience, and proficiency in English (IELTS 7.0 overall, minimum 6.5 in each component). Strong quantitative skills, experience in statistical modelling, machine learning, climate data analysis, or environmental modelling, and proficiency in Python or R are essential. The ability to work across disciplines and engage with stakeholders is highly valued. Funding includes full tuition fees and a stipend (bursary). The application deadline is May 27, 2026. For further details or to discuss your suitability, contact [email protected]. Applications should include all supporting documents, a covering letter, and a 2000-word statement outlining your expertise and interest in the project. Apply via the FindAPhD project link.