Available Projects
The project list below helps self-funded and externally sponsored applicants (scholarship) connect with a prospective supervisor.
Applications are welcome from those who can support themselves or have funding already arranged. Such applications will go through the same level of academic assessment. Please see the fee arrangementsLink opens in a new window for PGR students.
On-line tracking of C. elegans: the development of an automated real-time tracking platform
Caenorhabditis elegans is a widely used model organism for studying behaviour, neurobiology, ageing, and disease. This project will develop an automated real-time tracking platform capable of continuously monitoring worm movement and behaviour. Combining computer vision, image processing, and machine learning, the research aims to deliver accurate, high-throughput behavioural analysis to support biological discovery and experimental automation.
Primary supervisor: Christopher James - c.james@warwick.ac.uk
Project detail:
Caenorhabditis elegans (C. elegans) is one of the most extensively studied model organisms in biological and biomedical research. Its simple nervous system, well-characterised genetics, and reproducible behaviours make it an invaluable tool for investigating fundamental questions in neuroscience, ageing, genetics, drug discovery, and disease mechanisms. Behavioural analysis plays a central role in many of these studies, yet the collection and interpretation of behavioural data often remain labour-intensive and dependent on offline analysis.
This PhD project will focus on the development of an automated real-time tracking platform for C. elegans, enabling continuous monitoring and analysis of behaviour as experiments are performed. The research will investigate advanced computer vision, image processing, and machine learning techniques to accurately detect, track, and quantify the movement of individual worms and populations within laboratory environments.
A key challenge is the reliable tracking of organisms under varying imaging conditions, including changes in illumination, organism density, posture, and movement patterns. The project will develop robust algorithms capable of identifying and following organisms in real time, extracting behavioural metrics such as locomotion, turning behaviour, exploration, activity levels, and responses to environmental stimuli. The platform may also incorporate automated event detection and behavioural classification, providing researchers with rapid and objective insights into experimental outcomes.
The successful candidate will work at the intersection of biomedical engineering, computer vision, artificial intelligence, and quantitative biology. Training will encompass image analysis, machine learning, software development, biological data analysis, and experimental research methodologies. Opportunities may also exist to integrate the tracking platform with automated microscopy systems and high-throughput screening technologies, creating powerful tools for large-scale biological investigations.
The resulting system has the potential to significantly enhance the speed, accuracy, and reproducibility of behavioural studies using C. elegans. Applications include phenotyping genetic variants, evaluating therapeutic interventions, studying neurodegenerative and age-related processes, and supporting automated laboratory workflows. By enabling real-time, high-throughput behavioural monitoring, this research will help accelerate biological discovery and contribute to the development of next-generation tools for experimental life science research.
How to apply for admission: www.warwick.ac.uk/pgrengineering
The University of Warwick provides an inclusive working and learning environment, recognising and respecting every individual’s differences. We welcome applications from individuals who identify with any of the protected characteristics defined by the Equality Act 2010.