PhD studentship: School of Computer Science

The Computer Vision Group is looking for an aspiring PhD to investigate multi-agentic AI, LLMs, and VLMs applied to agricultural sciences. Currently, established AI models often fail to generalize in agricultural applications, especially when tested with data that is different from their training setting, even in subtle ways.

This studentship is fully funded for 3.5 years from 1st October 2026. (Home applicants only).

In this Ph.D. project, you will advance this research field by investigating how to develop, design, and evaluate domain specific multi-agentic AI models and systems that can plan and execute tasks with multi-modal heterogeneous data (e.g. text, location, and images), associated with diverse applications, such as earth observation, climate, and phenotyping. Developed models will be tested for a variety of highly relevant problems in agriculture, like crop type classification, crop yield forecasting, field boundary delineation, crop disease, and crop failure detection. The Ph.D. research builds upon recent advancements in multi-agentic AI systems. Processing and integrating multiple data modalities will also be key to the research objective of developing dynamic and intelligent systems that provide further insight into modern agricultural applications and food security problems.

You will work with an interdisciplinary and international team of experts in artificial intelligence (e.g. computer vision, deep learning, AI) and green life sciences (e.g., remote sensing, crop modelling, and food security), within the European funded project AgriscienceFM (Horizon programme), which has recently been awarded by the European Commission. For information about this project can be found here: https://www.agriscience.fm  

Your duties and responsibilities: 

• Familiarise with the state-of-the-art in multi-agentic AI, and how to interact with external models and tools.
• Design, develop, and evaluate multi-agentic AI model architectures to gain and improve our insights in agriculture from analysing multi-modal data.
• Use Anthropic and/or OpenAI APIs.
• Perform large-scale training and testing on an HPC server.
• Disseminate the research results by writing papers and presenting your work at international conferences.
• Collaborate with other project partners in joint tasks, and contribute to the overall project success.

You will be supervised by Valerio Giuffrida (see email below) and one other member of the academic staff within CVL.

What are we looking for?

You are highly motivated, self-driven, and curious to advance use-inspired artificial intelligence methods. You bring along your enthusiasm to work in a highly dynamic, international team towards a common objective. 

In addition: 

• A successfully completed BSc/MSc degree in computer science, artificial intelligence or engineering, or a similar relevant field.
• Proficiency in programming in Python and experience in PyTorch, Scikit-Learn or related modern machine learning libraries.
• Some working knowledge of using Anthropic/OpenAI APIs.
• Good writing skills, or contributions to scientific papers.

Funding

Annual tax-free stipend based on the UKRI rate (£21,805 for 2026/27) plus fully-funded Home PhD tuition fees for the 3.5 years.

Entry Requirements

2:1 Bachelor or Masters degree or international equivalent in computer science, artificial intelligence, or engineering (or related discipline). Studentships are open to home students only. 

Application Process

Applications to be informally made direct to the Valerio Giuffrida Valerio Giuffrida at valerio.giuffrida@nottingham.ac.uk first.

Post interview, application to be made through the MyNottingham system stating the supervisor’s name and project title. The deadline to have completed and submitted your formal application is Friday 29th May 2026. 

Enquiries to be directed to: Valerio Giuffrida - valerio.giuffrida@nottingham.ac.uk

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