BARIToNE: Data-Driven Crop Breeding for Climate-Resilient Barley (Project code 25L)

About the Project

Lead supervisor - Dr Paul Shaw, James Hutton Institute

Additional supervisors - Sebastian Raubach, James Hutton Institute; Dr Hajk Drost, University of Dundee; Dr Miguel Sanchez Garcia, ICARDA, Morocco

Industry supervisor - Dr Benjamin Kilian, Crop Trust

Location

This project will be based at the James Hutton Institute, Dundee and the appointed student will register at University of Dundee as the degree awarding institution.

The project

Modern crop breeding faces an immediate challenge: how to deliver resilient, high-yielding varieties fast enough to keep pace with climate change.

This PhD project combines crop genetics and data-driven decision making, with a strong emphasis on biologically grounded questions and practical relevance to breeding programmes.

You will work with real barley data to understand how genetic relationships, historical selection, and environmental context shape breeding outcomes. This understanding will then be used to determine which statistical and computational approaches can support a better decision making to find more adaptable crop varieties for a particular local field.

No prior experience in machine learning or artificial intelligence is required. The project is designed to build confidence step-by-step, starting from familiar data science and statistical approaches and gradually introducing more advanced modelling methods where appropriate.

You will:

• Analyse crop data from barley breeding programmes
• Use R and related data science tools to explore inheritance patterns, population structure, and trait prediction
• Learn how to develop interpretable statistical and computational models that link data to breeding outcomes
• Learn how predictive approaches (including ML-based methods) are used responsibly and transparently in applied crop science
• Collaborate with crop scientists and breeders to ensure results remain biologically meaningful and practically useful

Throughout the PhD, emphasis is placed on understanding the biology first, with computational tools used to answer well-defined scientific questions.

Training and support

This project offers structured, supportive training, including:

• Core supervision from experts in crop genetics, quantitative biology, data analysis, and AI
• Gradual introduction to machine learning concepts, tailored to your background and pace
• Opportunities to attend methods workshops, summer schools, and conferences
• A collaborative supervisory environment where questions are encouraged and expectations are made explicit

You will not be expected to "already know everything". The goal is to grow expertise over time, not to test prior knowledge.

We welcome applications from candidates who:

• Have a background in plant science, crop science, biology, environmental science, computational biology, bioinformatics, statistics or related disciplines

How to apply

Applications to the BARIToNE CTP programme are made via the form which can be found on our website: https://baritone.hutton.ac.uk/how-to-apply/

The application deadline for this round is 11.59pm on 28th May 2026 but interviews may be arranged as soon as eligible and suitable candidates apply.

Funding Notes

If you are successful, you will receive a full UKRI stipend (currently £20,780) also covering tuition fees, training, and travel budget. We also offer enhanced support to individuals with primary care responsibilities or disabilities.

This round of applications is only open to those with UK residency status. Students must meet the eligibility criteria as outlined in the UKRI T&Cs (View Website) (see TGC 5.2).