Understanding rodent behaviour is a critical step in basic and preclinical research on conditions such as ocular diseases, Alzheimer’s disease, anxiety and depression. Treatment efficacy is determined by changes in rodent behaviour. To quantify those changes represents an ongoing challenge. Software currently used in academia and industry can only perform low throughput analyses that enable to determine the position of the animals but not what the animal is doing.
In the last few years substantial advances have been made in improving accuracy and resolution of body tracking (e.g. ) and behavioural classification (e.g.). Building on some of these results we recently developed a computational technique based on a Statistical Shape Model to reconstruct 3D body poses in freely moving animals . The successful PhD applicant will further develop the mouse Statistical Shape Model and use it to classify the variety of unconstrained mouse behaviours.
Neuronal correlates of such behaviours will also be investigated by simultaneous recordings in freely moving animals. Building on our experience in freely moving recordings  and on the high throughput neuropixel system  the successful candidate will study the impact of body movements, quantified with a Statistical Shape Model, on visual processing
Candidates are expected to hold (or be about to obtain) a minimum upper second-class honours degree (or equivalent) in engineering/physics/computer science/mathematics. Candidates with strong interest and previous experience in vision, computer vision or system neuroscience are encouraged to apply. Good coding skills (MATLAB/Python) are an important requisite.