Research Associate/Senior Research Associate in Machine Learning

Bristol (City Centre), United Kingdom
£33,797 - £42,792
Friday, 1 November 2019
End of advertisement period
Saturday, 21 December 2019
Contract Type
Full Time

Division/School School of Computer Science, Electrical and Electronic Engineering and Engineering Maths
Contract type Open Ended
Working pattern Full time
Salary £33,797 - £42,792
Closing date for applications 21-Nov-2019

University of Bristol is seeking an exceptional candidate to take up a research position on the PD-SENSORS project, with funding until September 2021, working on state-of-the-art machine learning for understanding the behaviour, and changes thereof, of Parkinson’s patients using data from a multi-sensor smart home platform.

The primary purpose of this position will be to advance the understanding of the progression of Parkinson’s disease via the use of machine learning to identify the fluctuations in the disease from the behaviour of the patient. To collect this data, we use capabilities built by the SPHERE project (a Sensor Platform for HEalthcare in a Residential Environment), funded by EPSRC, which has been developing a unique integrated platform of sensors to deploy in people’s homes to monitor their health and wellbeing during everyday life (  

This project aims to apply, and build on, existing machine learning methods for behavioural modelling using multiple sensor modalities, including RGB-D cameras, wearable accelerometers and appliance monitors. We are specifically interested in detecting changes and variations in human behaviour over both short and longer periods of time, within the context of Parkinson’s, using these modalities.  We envisage that the usage of the multiple available data modalities will help build a better model of behaviour and lead to better insights into the disease progression. Behaviours we are interested range from sleep to Activities of Daily Living (ADL).

The main research area for this post will be multi-modal time series analysis with state-of-the-art machine learning techniques, e.g. deep learning, using data collected from both Parkinson’s patients and control subjects within the SPHERE house, in order to further our understanding of the progression of Parkinson’s disease.

The successful application should have a strong background in Machine Learning, Data Mining or similar, to a PhD level or beyond. Experience of working in multi-disciplinary projects, particularly within healthcare, would be advantageous.  The successful applicant will possess strong written and oral communication skills and will be expected to present results at international conferences and publish findings in international journals.

It is anticipated that interviews will take place within 2 weeks of the closing date.

Informal enquiries can be made to:

Dr. Roisin McNaney ( for enquires related to the healthcare aspect of the project.

Dr. Ryan McConville ( for machine learning related enquires.

We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and BAME communities, to join us.