[Jobs] Funded PhD studentship (UK home-fee only): Autonomous AI Robotics for Soil Pathogen Detection (Industry Collaboration with Syngenta)

[Ayse Kucukyilmaz (staff)]

Dear Colleagues, 

We are seeking a highly motivated PhD candidate to join our robotics team at University of Nottingham as part of the AI Doctoral Training Centre in collaboration with Syngenta, a global leader in crop protection, offering access to real datasets, domain expertise, and the opportunity for an industrial placement. The student will work on an ambitious interdisciplinary project at the intersection of robotics, artificial intelligence, and biosciences, developing next-generation autonomous systems for precision agriculture and food security.

Due to funding restrictions, this studentship is open to UK Home-fee eligible candidates only.

PhD title: Autonomous AI Robotics for Soil Pathogen Detection (AI DTC project, start October 2026)

Location: University of Nottingham, UK

Start Date: October 2026

Supervisors: Dr Ayse Kucukyilmaz, Prof Rumiana Ray, Dr Aly Magassouba

Final shortlisting application deadline: Friday 10 April 2026

Summary: The project will design and deploy manipulation, perception, and decision-making algorithms on a quadruped robot platform (e.g. Unitree Go2, Boston Dynamics Spot) capable of autonomously exploring agricultural environments, collecting soil samples, and supporting early detection of soil-borne pathogens. The research will focus on enabling robust field deployment through AI-driven exploration and sampling strategies, adaptive manipulation for soil interaction, and effective human–robot interaction (HRI) to support collaboration with farmers and soil scientists. The robot will learn where, when, and how to sample soil by combining prior agronomic knowledge, real-time sensor data, and historical pathogen datasets. The long-term vision is a deployable robotic assistant that supports early pathogen detection, reduces manual labour, and enables precision intervention.

Key Scientific Questions

• How can autonomous robots robustly manipulate and interact with soil in unstructured agricultural environments?
• How can heterogeneous data sources (robot sensory data, spatial field data, and biological soil detection outputs) be fused to guide decision-making in situ?
• How can AI-driven exploration and reinforcement/active learning strategies optimise soil sampling for early pathogen detection under real-world field constraints?
• How can learning-based robotic systems generalise across fields, crops, and environmental conditions?
• What forms of human–robot interaction best support trust, interpretability, and effective collaboration between robotic systems, farmers, and soil scientists?

Candidate Profile

We are looking for candidates with:

• A strong background in Computer Science, Robotics, AI, or a related field
• Strong programming skills (e.g., C++, Python, ROS2, or similar frameworks)
• Enthusiasm for real-world robotics deployment and interdisciplinary research
• Experience in robotics, field systems, or data-driven modelling is beneficial but not required. Training will be provided across disciplines.
• Interest in precision crop protection and/or diagnostics at the point of care (field) is highly desirable.

How to Apply

Please email the academic supervisors with a subject line “AI DTC studentship: Autonomous Robotic Soil Pathogen Detection”

Attach:

• CV
• Academic transcripts
• A short personal statement explaining why you are interested in this research topic and your motivation for doing a PhD

Dr Ayse Kucukyilmaz - ayse.kuc...@nottingham.ac.uk

Prof Rumiana Ray - rumia...@nottingham.ac.uk

Dr Aly Magassouba - Aly.Mag...@nottingham.ac.uk

Suitable candidates will be interviewed and, if successful, invited to submit a formal application.

Final shortlisting application deadline: Friday 10 April 2026

Please feel free to distribute. 

Best wishes, 

Dr Ayse Kucukyilmaz 

Associate Professor 

 

School of Computer Science

University of Nottingham

Room B31

Jubilee Campus

Nottingham, NG8 1BB 

w: http://www.cs.nott.ac.uk/~pszak1/ 

Book time to meet with me

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