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PhD opportunity @ University of Manchester Metropolitan University

Dog behavior & AI funded by dogs trust


Apply here: link


"A great fully funded PhD opportunity in partnership with Dogs Trust! If you’re into AI and dog behaviour and want to work on a project which could have a considerable impact on dog welfare and with a great supervisory team from Manchester Metropolitan Uni- this PhD may be for you." - originally posted here


This project provides an annual stipend of £19,237. 

Project advert

In the UK, 130,000 dogs are housed in animal rescue centres at any one time. Monitoring their health and behaviour is a major welfare challenge, as assessment is often conducted via labour-intensive manual observations by centre staff and veterinarians. Assessments may lack consistency, and behavioural testing can be stressful for animals. Automated video-based monitoring of dog activity levels and behaviour within kennels is a potential solution.

This project aims to revolutionise welfare assessment in kennels by developing an objective, easy-to-use video analysis tool, leveraging deep learning techniques. You will implement cutting-edge convolutional AI and deploy explainable techniques. Building on our ongoing work in controlled laboratory settings, where we have demonstrated the ability of deep learning to accurately track dog movements across various breeds, we will now translate this research into a practical tool deployable by non-specialists in rescue centres.

You will partner with Dogs Trust, the UK’s largest dog welfare charity, operating twenty-three rehoming centres across the UK and Ireland. Dogs Trust uses evidence-based approaches to improve dog welfare and actively participates in scientific research. Their extensive expertise, ethical commitment and nationwide influence will be invaluable in developing and implementing our AI-powered research to advanced animal welfare assessment.

Project aims and objectives

Aim:The proposed research project aims to develop an advanced, non-invasive machine vision tool for the assessment of canine behaviour and welfare in kennel environments.

Objectives:The research objectives are to:

  1. Develop an automated video triaging pipeline using pre-trained object detection models to confirm dog presence and exclude humans in footage, optimising data storage and transfer.

  2. Create “KennelNet,” a deep neural network for tracking dog posture using annotated video data, enabling detailed behavioural analysis across diverse breeds and environments.

  3. Implement deep clustering techniques to identify discrete canine behaviours from posture data, enhancing explainability and validating the system against wearable sensor data.

  4. Establish a real-time tracking and behavioural analysis system using lightweight networks and advanced filtering techniques.



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