University of Cambridge
Growing AI PhD Studentship
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Project Overview
Resilient and productive agriculture is reliant on bespoke planning involving data-driven decisions guided by local practice, required practice, local data, such as crop yield, and global data such as commodity prices and weather predictions. Technological progress has made it possible to automatically collect a variety of sensor data and self-reported practice. The challenge is to understand how to leverage such information to design an AI-bestowed system that provides bespoke advice to the individual crop producing farmer about strategy and practice that will deliver a profitable and environmentally restorative farming system.
This PhD position is funded and supported by a collaborative agreement between Hutchinsons and the charity LEAF (Linking Environment And Farming). Both organisations have complementary goals in terms of improving the way collected and available data is leveraged to improve farming practices. The successful candidate will have access to experts and data from these organisations, and will explore the development of a human-centred decision support system leveraging multimodal data and artificial intelligence.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
The ultimate goal for the project is to support farmers with individualised and actionable insights, allowing them to make informed decisions in relation to the profitability and sustainability of their farming practices.
Ideal Candidate Profile
We welcome applications from motivated students with a strong background in engineering or computer science.
The Ideal Candidate Will Have
- Strong programming and software skills.
- An awareness of machine learning theory and techniques.
- Experience of working with large multimodal datasets.
- Interest in human-computer interaction and human-centred system design.
- Strong communication and organisational skills.
While it is not necessary to have prior knowledge of farming practices, this would be advantageous for the project.
Application Process
This studentship is available to eligible home students and will cover fees and a stipend. Applicants should have (or expect to obtain by the start date) at least a good 2.1 degree in Engineering or Computer Science. Please also refer to details listed under Ideal Candidate Profile.
To apply for this studentship, please send your two-page CV and one-page covering letter outlining your suitability to Per Ola Kristensson (pok21@cam.ac.uk) and John Dudley (jjd50@cam.ac.uk) to arrive no later than 5 pm BST on Friday 24 July.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Applications may close early if the position is filled before this date. Please include "[CAM-GROWING-AI]" in your email subject. Interviews will be held in the week of Monday 27 July. Please note that any offer of funding will be conditional on securing a place as a PhD student. Candidates will need to apply separately for admission through the University's Postgraduate application portal. Preferred candidates will be asked to submit an application to the postgraduate application portal by 30 July 2026. The applicant portal can be accessed via: www.postgraduate.study.cam.ac.uk/courses/directory/egegpdpeg.
Please note there is a £20 application fee attached to using the Cambridge Postgraduate Application Portal.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills