Research Fellow (Machine Learning)

Fixed-term available from 1 May 2024 to 30 April 2026.

Full Time - 35 Hours Per Week.

The Centre for Medical Informatics at the Usher Institute within The University of Edinburgh is looking for a postdoctoral researcher with experience in signal processing and machine learning for monitoring emotional states and wellbeing.

The Opportunity:

The researcher will have the opportunity to contribute to the design and implementation of advanced machine learning and signal processing software for health and wellbeing monitoring based on biological, speech and other behavioural signals, as part of the Horizon Europe project INT-ACT. Our team at University of Edinburgh will lead the collection and analysis of wearable sensor data and spontaneous speech narratives related to cultural heritage experiences mediated by extended reality software. The post holder will also contribute to the integration of the monitoring software to an extended reality (XR) system to be implemented by another consortium partner. The post-holder will collaborate with a qualitative methods researcher at the University of Edinburgh, and with teams in four countries (Finland, Greece, Portugal, and Romania) to co-create and implement a system to support interaction with intangible cultural heritage across diverse sites in Scotland, Portugal, Finland and Greece as a means of enhancing healthy ageing, mental wellbeing, and social connectedness.  

Informal enquiries may be directed to Saturnino Luz, Professor, Chair of Digital Biomarkers & Precision Medicine (s.luz@ed.ac.uk).

Your skills and attributes for success: 

• PhD in Computer Science, Computer Engineering or related field (or considerable relevant and equivalent experience).
• Experience with machine learning methods for analysis of speech, sensor and behavioural data.
• Excellent machine learning and signal processing skills.
• Track record in designing and implementing systems for tracking of mood or emotions, or applications of multimodal data to health and wellbeing assessment.
• Excellent programming skills.

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