Postdoc: probabilistic ML and intensive care unit data

We are seeking a postdoctoral researcher at the University of Edinburgh to work on a project to develop and validate advanced statistical methods for analyzing time-series data from adult neuro-intensive care unit (NICU) patients.

The project would be suitable for candidates with a strong background in probabilistic machine learning who are keen to work on a challenging application area.

The researcher will be a part of the School of Informatics at the University of Edinburgh. This is an opportunity to work in a world-leading machine learning group, including seven faculty in the area. More broadly, a recent international review described the School as an “elite” department of computer science in Europe, and in national research assessment exercises, the School of Informatics has consistently ranked at the top in the UK for research quality.

In the first year the Research Associate will focus on methods for inferring physiological and artifactual events from time-series data, including data cleansing, anomaly detection, and inference in probabilistic models. This work will build on that of Quinn, Williams and McIntosh (PAMI, 2009) on Factorial Switching Linear Dynamical Systems applied to Physiological Condition Monitoring. In the second year of the project the models will be validated against live data collected at the NICU in the Southern General Hospital (Glasgow), and development of the models continued in light of the results obtained.

The postdoctoral researcher will be supervised by Prof Chris Williams , who may be contacted for informal enquiries.

For more information about the project and information about how to apply, please see


Please note the closing date of THURSDAY 10 JANUARY 2013, at 5pm UK time.

Chris Williams
Institute for Adaptive and Neural Computation School of Informatics, University of Edinburgh
10 Crichton Street, Edinburgh EH8 9AB, UK
tel: +44 131 651 1212 fax: +44 131 650 6899