|Title: Funded Doctoral Position in Montreal: Deep Learning for Visual Recognition ||Posted: August 5, 2019|
|Company/Institution: ETS Montreal|
|Location: Montreal, Canada|
|Department: Systems Engineering|
Description: Applications are invited for a funded doctoral position in deep learning for visual recognition applications. The candidate will work under the supervision of Profs. Pedersoli and Granger at the Laboratory of imaging, vision and artificial intelligence (LIVIA), ETS Montreal (University of Québec). This funded position is available immediately, for a duration of 4 years, and offers a possibility for collaborations-internships with top research companies and institutions in Montreal and abroad. The salary is competitive and exempted of taxes.
We are looking for a highly motivated doctoral student who is interested in performing cutting-edge research on machine learning algorithms applied to the visual object recognition, with a particular focus on deep learning architectures (e.g, auto-encoders, convolutional and recurrent neural networks), domain adaptation, and weakly-supervised learning. Application domains of interest include expression recognition in health monitoring and person re-identification in video surveillance. Prospective applicants should have:
• strong academic record with an excellent M.Sc. degree in computer science, applied mathematics, or electrical engineering, preferably with expertise in one or more of the following areas: machine learning, computer vision, pattern recognition, artificial intelligence;
• a good mathematical background;
• good programming skills in languages such as C, C++ and/or Python. Knowledge of deep learning frameworks would be a plus.
• publication in one of the major conferences or journals in computer vision and machine learning would be an asset.
Application Instructions: Application process: For consideration, please send your CV, research statement, names and contact details of two references, transcripts, a link to a M.Sc. thesis, as well as relevant publications to:
Further information: Marco Pedersoli & Eric Granger