Fungicide resistance is an important and growing problem. Fungicides are very expensive to develop, and so stewardship of effective chemistry is paramount. Mathematical models play a key role in designing resistance management strategies, since the coupled epidemiological-evolutionary dynamics are difficult to study by other means. By generalising the current resistance management theory to the important case of monocyclic pathogens, this project addresses a key gap in our current understanding.

This PhD project will develop mathematical models of fungicide resistance in monocyclic plant pathogens. The work builds on earlier modelling efforts within the group (e.g., Elderfield et al., 2018; Taylor et al., 2023a,b) but shifts the focus to pathogens with very different epidemiological characteristics. The outcomes of this research will be relevant to the optimisation of current fungicide strategies and could have long-term implications for agricultural disease control.

Existing theory for resistance management is almost entirely based on diseases that cycle repeatedly during a growing season. However, many important pathogens—including Sclerotinia sclerotiorum (affecting vegetable crops) and Oculimacula yallundae/acuformis (causing eyespot in cereals)—are monocyclic, completing only one infection cycle per season. These diseases are typically managed using fungicides, yet the dynamics of resistance development in these systems remain poorly understood.

The student will adapt and extend existing fungicide resistance models to reflect the biology of monocyclic pathogens, incorporating within-season pathogen development and other relevant features. The work will use case study systems informed by data and expertise from our project partners at ADAS (https://adas.co.uk/), the UK’s largest independent provider of agricultural and environmental consultancy. The student will also spend six months working on-site at the ADAS research station in Boxworth (near Cambridge).

The project will be based in the Theoretical and Computational Epidemiology Group at the University of Cambridge, led by Prof. Nik Cunniffe. The student will be co-supervised by Drs. Corkley, Grimmer and van den Bosch at ADAS, who are all actively engaged in fungicide resistance research (e.g., van den Bosch et al., 2014, 2020; Grimmer et al., 2015; Corkley et al., 2025). Dr Grimmer also serves as Secretary of FRAG-UK (Fungicide Resistance Action Group UK), providing a direct link to nationallevel guidance and impact.

Applicants with a background in mathematics, physics, engineering, or theoretical ecology are particularly encouraged to apply. Prior experience with programming is beneficial but not essential. Candidates from experimental or biological backgrounds with a strong interest in modelling are also welcome.

Training provided:

• Mathematical modelling of biological systems
• Simulation and analysis of epidemiological models
• High-performance computing (HPC)
• Data integration and model fitting
• Collaboration across academic and applied research contexts

To apply for this project please see the information on the BBRSC website: https://bbsrcdtp.lifesci.cam.ac.uk/how-apply#iCase Please don't apply for the PhD in Plant Sciences but follow the BBSRC iCASE instructions.