The postdoctoral fellow will join the Adaptive Systems group at the Division of Vehicle Engineering and Autonomous Systems, at the Department of Applied Mechanics. The postdoctoral work may also include interaction with researchers at other institutes and at several industries in the region. The employment is for one year, with possibility for a one-year extension. The starting date will be around May 2011.
Job description
The postdoctoral fellow will work in the field of driver sleepiness detection. Driving while sleepy, and falling asleep at the wheel is a common phenomenon, contributing to many vehicle crashes. Several systems for on-board monitoring of sleepiness in drivers have been proposed in recent years, but the problem of reliably detecting driver sleepiness still remains large unsolved. The content of the postdoctoral project is as follows:
- Predicting driver sleepiness related crashes Most of the research on driver sleepiness detection has been focused on estimating the momentary level of sleepiness in the driver. However, the ultimate goal of a driver sleepiness detection and warning system is to prevent sleepiness related crashes. In the Adaptive systems group, some work has been carried out regarding the prevention of actual crashes. The postdoctoral fellow will continue this work, with the aim of devising a system for predicting driver sleepiness related crashes, using a machine learning framework developed in the Adaptive systems group.
- Modeling individual differences Several studies have demonstrated that there are significant individual differences in behavioral changes due to sleepiness. Therefore, a better understanding of individual differences in driving behavior due to sleepiness is fundamental for a reliable driver sleepiness detection system. The postdoctoral fellow will work with these issues as well.
In both parts of the project, the work will also include further development of two methods developed in the Adaptive systems groups, namely (i) blink-based sleepiness indicators and (ii) sleepiness detection systems combining several different indicators.
Required qualifications
The applicant should have a PhD or equivalent (at the starting time, i.e. in May 2011), in the topic of driver sleepiness or related fields. Furthermore, the applicant must have strong knowledge in (i) stochastic optimization methods as well as various other machine learning methods (including artificial neural networks), (ii) analysis of driving data and, in particular, analysis of camera-based (e.g. blink) data, (iii) the scientific concepts of sleep, in general, and sleepiness while driving, in particular.
Application procedure
The application shall be written in English and include the following items:
- An application of a maximum of one A4 page containing your specific qualifications for the position
- Curriculum Vitae including list of publications
- Two reference persons who can be contacted by Chalmers (describe association with them and give their contact addresses)
- Attested copies of education certificates, including grade reports and other documents
The application shall be sent electronically.
The documents according to items 1-4 above shall be attached as two pdf-files.
One should contain items 1-3 in the listing of material to be included in the application The other should contain item 4 in the listing of material, and any other appendices.
The files may be compressed (zipped).
If any material is not available electronically or cannot be transferred to pdf format, the material can be sent as a hard copy to Registrar. The applicants name and the reference number (2011/9) must be written on the first page of the application.
Address:
Registrar
Chalmers University of Technology
SE-412 96 Göteborg
Sweden
Further information
Mattias Wahde
Phone: +46 31 772 37 27
E-mail: mattias.wahde@chalmers.se
Malin Kjellberg
Phone: +46 31 772 13 76,
E-mail: malin.kjellberg@chalmers.se
Reference number 2011/9
Application deadline : March, 15th 2011
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Disclaimer: Every effort has been made to ensure the above information is current and correct. However, applicants should contact the appropriate administering body before making an application, as details do change frequently.
