The Idiap Research Institute, affiliated with École Polytechnique Fédérale de Lausanne, seeks one PhD student in statistical learning to develop original techniques for vision with complex priors.
This position is funded by a grant from the Swiss National Science Foundation, and the candidate will be a doctoral student at EPFL EDEE doctoral school. Research will be done under the supervision of Dr. François Fleuret.
Object detection and recognition techniques based on machine learning have historically relied on crude prior representation of the image, far from the complexity and richness of biological systems.
This project will investigate an alternative approach using very rich feature extractors addressing multiple modalities of the signal. The objective is to create new tools to help the design of such feature extractors, and to investigate learning techniques able to cope with very large and heterogeneous families of features.
The objective is to design novel approaches to full-scene interpretation, aiming at detecting many objects visible in an image.
This work will mix theoretical developments in statistical learning with the implementation of algorithms working on real-world data. Applicants must have a strong background in mathematics and be familiar with several of the following topics: probabilities, applied statistics, information theory, signal processing, optimization, algorithmic, and C++ programming.
The Idiap Research Institute is located in Valais, a scenic region in the south of Switzerland, surrounded by the highest mountains of Europe, and within close proximity to Lausanne and Geneva. The working language of Idiap is English.
Please contact francois.fleuret (at) idiap.ch for additional information.