Research Associate: Machine Learning and the Analysis of Medical Sensor Data

Fixed Term: 2 years with possible extension.
Grade: UE07 (£30,424 - £36,298)

Closing Date : 13-Jan-2014

Contact Person : Irene Madison

Contact Number : 0131 650 2723

Contact Email : infhr@inf.ed.ac.uk

Applications are invited for an experienced researcher to develop and validate advanced statistical methods for the analysis of data from a novel multiplexed sensor in the lungs and blood vessels. The post is part of a large Interdisciplinary Research Collaboration which involves: the development of novel chemical sensors and detectors; the development of inference methods to analyse the data produced and infer the concentration of various pathological processes present so as to provide doctors with information on the state of intensive care unit patients; and the testing of the methods in in-vitro, ex-vivo and in-vivo assay systems. The post will be supervised by Professor Chris Williams, School of Informatics, University of Edinburgh.

The successful candidate will be a probabilistic machine-learning researcher (or similar) keen to work on a challenging application area. Over the duration of the project the richness and complexity the sensed data and the number of data sources will increase. The integration of information collected from different model systems will be tackled using hierarchical Bayesian models. Professor Williams has extensive experience of modelling and inference in an ICU setting, see e.g. the work on factorial switching linear dynamical systems (Quinn, Williams and McIntosh, IEEE Trans on Pattern Anal Mach Intell, 2009).

You will be self-motivated with the ability to take day-to-day responsibility for the progress of the proposed work and collaborate effectively with project partners from medicine, physics, chemistry and engineering. There is potential for innovative methodological developments in the modelling framework. The post offers the opportunity to work in a world-class machine-learning research environment, applying and extending cutting edge methods in an important application area. 

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