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MOBIO - Mobile Biometry

Secured and Trusted Access to Mobile Services

European Funded Project (FP7-2007-ICT-1)
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Job Opportunities

Link New opening for an Internship position for the development of a face recognition iPhone App
This project aims to integrate latest Idiap scalable face detection and recognition technology on an iPhone by developping an innovative demonstration application.
Link PhD Position on Face Recognition at IDIAP (filled)
The IDIAP Research Institute seeks a qualified candidate for a PhD student position in the field of face recognition and model adaptation. Position is available now. Model adaptation, also called on-line adaptation, is concerned with maintaining a biometric system when it is deployed over an extended period of time. Because the system may degrade over time, the template (or model) representing the user has to be updated. The doctoral student will investigate model adaptation techniques for face recognition. A range of fundamental problems in computer vision and biometric authentication, such as object recognition, discovery of invariant visual patterns, and domain-specific problems are also open for investigation. The ideal PhD candidate will hold a degree in computer science or related fields. She or he should have strong background in statistics, linear algebra, signal processing, C++ programming, Perl and/or Python scripting languages, and Linux environment. Knowledge in statistical machine learning and vision processing is an asset.
Link Post-Doc Researcher at Uni Manchester (filled)
We are seeking two individuals with a strong background in Computer Vision research and who have an interest in extending their experience to the implementation of facial interpretation methods. This is a very exciting opportunity to join a large team as part of an EU funded project into implementing biometric technologies on mobile phones (MOBIO). Successful applicants will work in the field of facial feature location in static images and video sequences, working with a growing team of people at the University of Manchester, lead by Prof Tim Cootes and Prof Chris Taylor. Considerable experience in Computer Vision Research, a good honours degree in a mathematical, computational or physical science and a PhD or equivalent are essential. A proven track record of team leadership skills will be an advantage as will post-doctoral experience in a relevant field.
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