PhD in Engineering, Physics, Computer Science, Mathematics, Statistics, Computational Neuroscience, or related areas) and be interested in brain signal processing. Thus, the position also includes practical laboratory work with the ear-EEG devices, and planning, implementation and execution of new recordings.Īpplicant must have a background in machine learning (e.g. Even though the project already has unique ear-EEG sleep recordings, a part of the position will be to participate in the ongoing effort of establishing a larger data set of recordings, and to acquire new data sets from a new version of the ear-EEG device. The main part of the research will be development of signal processing and machine learning algorithms and methods for sleep assessment based on ear-EEG. The position is available from October 2018 or as soon as possible hereafter. The postdoctoral researcher will be a part of the Neurotechnology group and will be supervised by Associated Professor Preben Kidmose. The research will be based on data from ear-EEG recorded over extended periods of time, where sleep labels, obtained from concurrently PSG recordings, are only available from a few nights for each subject.
The research will focus on semi-supervised learning methods to develop sleep assessment algorithms tailored to the individual subjects. The aim is to develop technology providing a less expensive, less obtrusive and more precise assessment of sleep. The research is a part of a project in the Neurotechnology group focusing on ear-EEG for sleep monitoring.
Department of Engineering at Aarhus University is inviting applications for a two-year postdoctoral position within signal processing and machine learning of sleep EEG.
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