Research Associate in Formal Modelling and Explainable AI for Health and Care
Applications are invited for a Research Associate in Formal Modelling and Explainable AI for Health and Care
Applications are invited for a Research Associate in Formal Modelling and Explainable AI for Health and Care in the School of Informatics. This post will contribute to the New Technologies of Care Programme within the Advanced Care Research Centre (ACRC, http://edin.care) at the University of Edinburgh.
This full-time position is fixed term for 36 months.
Informal enquiries to be directed to Jacques Fleuriot (firstname.lastname@example.org) and Petros Papapanagiotou (email@example.com)
Salary grade scale: UE07 (£33,797 – £40,322pa)
Feedback is only provided to interviewed candidates.
Closing date 5pm (GMT) on 14th October 2020.
In order to allow for meaningful inference and decision making in the context of advanced care delivery for the older person, explainable computer-based models are needed that can capture the vast body of knowledge both around general and specific care pathways and incorporate data from real-time sources such as sensors. In the case of the person in later life, there is a need for contextualisation when it comes to both the care recipient and their environment.
One objective of this project is to build computer-based models that enable both the management and analysis of care, while supporting decision making via Explainable AI methods. Workflow management systems will be used to automate parts of care delivery, so that we can maintain consistent, accountable and continuous levels over time. AI-based workflow analytics and other techniques will be used to understand and predict outcomes based on sensor, video and recorded care data (coming from other streams of the New Technologies of Care Programme and the broader ACRC). Bridging the gap between the high complexity of the care landscape, the individual needs and circumstances of the older person, and the development of computer-based workflows that interface between data and processes in order to drive decision-making will be one of the important goals of this multidisciplinary project.
Further information and the application procedure are available here.