PhD on Towards an open-source, equipment-agnostic framework for automated welfare monitoring in the home cage

Your project will focus on the development of computer vision/machine learning techniques to monitor the behaviour of mice in the home cage, with a view towards developing a general-purpose open-source framework for the animal research community. Anomaly detection is a technique whereby rare or abnormal events are classified as deviations from what is otherwise expected, based on an existing body of “normal” data. Unsupervised deep learning techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), offer great promise for the detection of anomalies in both image and video data. You will develop techniques along these lines using hundreds of hours of video footage acquired of mice via an infrared camera. This project will provide you with ample opportunity to explore a wide range of techniques for the detection and localisation of abnormal behavioural events using real-world data.

The University of Lincoln is seeking to appoint a motivated, inquisitive PhD candidate to join a diverse team of researchers in the School of Computer Science. You will have a masters (ideally) or bachelor’s degree (2:1 or above, with honours) in computer science, engineering, bioinformatics, or a related discipline. You should also demonstrate one or more of the following in your application:

- Working knowledge of Python, MATLAB, or a similar high-level programming language
- Familiarity with classical image/video processing and computer vision techniques
- Experience in using one or more deep learning libraries (e.g., Keras, TensorFlow, Torch)
- Contributions to open-source software projects related to imaging/vision/machine learning
- 1+ years’ experience in machine learning or (bio)statistics, either in academia or industry

Candidates will need to be from the UK or EU.

Salary: 3 year PhD with standard MRC stipend

Closing date: 24/02/2020

Further information can be found here.