About the Project
Background
Based in the new Oxford University Big Data Institute, the aim of this work is to build open source technologies, including apps, that are able to assess cognition and behaviour of relevance to conditions in Brain Health. It will focus on some of the most important conditions facing society today, like Alzheimer’s Disease, which is estimated to cost the UK £26 billion per annum. Digital phenotyping methods have a huge potential to track cognitive trajectories remotely, longitudinally, continuously, precisely, economically and at population scale. This makes them especially appropriate for measurement at the prodromal stage. They have widespread applicability, for example, in the development of more effective clinical trials, but also as the building blocks for precision public health. Recently, a number of citizen science projects have emerged which make digital phenotyping methods available to large, open, populations of participants who never attend traditional in-clinic visits. These have huge potential, but also introduce many challenges. This project will investigate, through practical studies, the utility of this new digital method of research.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The work will entail developing technologies, either active or passive, to detect subtle longitudinal changes in behaviour and cognition. It will involve designing apps that can be used to engage large, remote populations, and evaluating their efficacy. It will be necessary to engineer solutions that can be deployed at significant scale. It will also involve designing studies that produce statistically robust outcomes, working with users and ethics committees to validate the approach, and analysing the collected data so as to maximise clinical insight. The research will need to tackle challenges from diverse and emerging fields including: digital phenotyping, informatics, statistics, gamification, cognitive psychology, passive measurement, and citizen science.
Supervision
This project would be supervised by Dr Chris Hinds, Professor Martin Landray and Professor John Gallacher.
Prospective Candidate
Candidates will be required to hold, or expect to gain, at least an upper second class honours degree or Master’s degree in computer science or a related discipline. Candidates must be able to demonstrate an outstanding aptitude for software engineering, a good understanding of statistics and epidemiology, and a strong interest in the design or evaluation of digital health technology. Experience with applied machine learning techniques, or cognitive psychology will be helpful.
Deadline
Thursday 1 June 2017.
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