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Andrew Dowsey

Top university

11 months ago

Funded PhD: Developing Hybrid AI on Wearables and Video for Fine-Grained Cattle Behavioural Monitoring University of Bristol in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Funded PhD Project (Students Worldwide)

Deadline

Expired

Country flag

Country

United Kingdom

University

University of Bristol

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Keywords

Computer Science
Data Science
Machine Learning
Deep Learning
Biology
Artificial Intelligence
Veterinary Science
Transfer Learning
Computer Vision
Animal Welfare
Internet Of Things
Agricultural Technology
Veterinary Pathology
Sensor Fusion
Surveillance
Wearable Sensors
Behavioural Biology

About this position

Are you passionate about revolutionising animal behaviour and welfare research? The University of Bristol invites applications for a funded PhD project focused on developing hybrid AI systems that integrate wearable sensors and video monitoring for fine-grained cattle behavioural analysis. This innovative project is based at the John Oldacre Centre, a state-of-the-art facility equipped with over 50 video cameras and advanced AI technologies to monitor and track cows in a dairy farm environment. The research aims to extend behavioural monitoring to open fields, where traditional video is impractical, by leveraging bespoke, fully programmable wearable sensors developed by the team.

As a PhD student, you will lead the development of a novel system that combines video and wearable sensor data using deep learning, particularly transformer models, to enhance our understanding of cattle behaviour both indoors and outdoors. Key research areas include transfer learning of behavioural classifiers from video to accelerometry data, optimisation of camera and sensor placement, and practical translation of the system for real-world use. The project is a collaboration between experts in data science, machine vision, animal science, and behavioural biology at the University of Bristol and Rothamsted Research’s North Wyke Farm Platform, a leading centre for sustainable livestock farming research.

This studentship is ideal for candidates with backgrounds in computing, mathematical sciences, machine vision, deep learning, or biosciences who are eager to develop new skills in AI and contribute to animal welfare. A tailored training package will support your academic and professional growth throughout the programme.

Funding: The project is funded for Chinese applicants through the China Scholarship Council (CSC) scheme. Applicants from other countries are welcome to apply if they can self-fund or have secured external scholarships. All applicants must submit a valid English language certificate by 1 December 2025. Nominees for the CSC must apply to the Council between 8 March and 1 April.

Eligibility: Applicants must meet the University of Bristol’s standard PhD entry requirements, including a relevant degree and English language proficiency. For detailed requirements, refer to the University of Bristol's PhD Veterinary Sciences page.

Application Process: Apply online via the University of Bristol portal, selecting 'Veterinary Sciences 4 year' as your programme. Indicate your funding source as either 'Studentship > China Scholarship Council' or 'Self-funded'. For queries, contact [email protected] or the project supervisor.

Key references: Computers and Electronics in Agriculture, bioRxiv preprint.

Funding details

Funded PhD Project (Students Worldwide)

What's required

Applicants must meet the standard University of Bristol eligibility criteria for PhD entry, including a relevant undergraduate or postgraduate degree (typically at least a 2:1 or equivalent), and a valid English language certificate. Backgrounds in computing, mathematical sciences, machine vision, deep learning, biosciences, or related fields are encouraged. For full details, refer to the University of Bristol's PhD Veterinary Sciences entry requirements.

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Professors

Professor at Bristol Veterinary School

University Name
Country flag

United Kingdom

Research Interests

Veterinary Pathology

Animal Science

Livestock Management

Computer Vision

One Health