Funded Doctoral Position in Montreal: Deep Learning for Video-Based Expression Recognition
Country/Region : Canada
Description
Applications are invited for a funded Ph.D. position in deep learning for spatiotemporal expression recognition from facial and vocal modalities. The candidate will work under the supervision of Prof. Eric Granger and at the Laboratory of imaging, vision and artificial intelligence (LIVIA), ETS Montreal (University of Québec) and Prof. Simon Bacon at Concordia University and the CIUSSS-NIM's Montreal Behavioural Medicine Centre. The position is available immediately after the candidate passes ETS application requirements, and for a maximum duration of 3-4 years.
We are looking for a highly motivated doctoral student who is interested in performing cutting-edge research in machine learning algorithms applied to expression recognition based on facial and vocal traits captured in videos, with a particular focus on deep learning (e.g, convolutional and recurrent neural network) architectures, domain adaptation and weakly-supervised learning. 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: affective computing, machine learning, neural networks, computer vision, face and speech processing, and pattern recognition;
- A good mathematical background;
- Good programming skills in languages such as C, C++, Python and/or MATLAB.
- Knowledge of deep learning frameworks would be a plus.
- A prior publication in one of the major conferences or journals in computer vision/machine learning is not necessary but would be very desirable.
Application process: For consideration, please send a resume, names and contact details of two references, transcripts for undergraduate and graduate studies, and a link to a Masters thesis (as well as relevant publications if any) to Eric.Granger-AT-etsmtl.ca.
Further information :
Eric Granger
http://www.etsmtl.ca/Unites-de-recherche/LIVIA
Simon Bacon
http://mbmc-cmcm.ca/member/simon-bacon/
We are looking for a highly motivated doctoral student who is interested in performing cutting-edge research in machine learning algorithms applied to expression recognition based on facial and vocal traits captured in videos, with a particular focus on deep learning (e.g, convolutional and recurrent neural network) architectures, domain adaptation and weakly-supervised learning. 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: affective computing, machine learning, neural networks, computer vision, face and speech processing, and pattern recognition;
- A good mathematical background;
- Good programming skills in languages such as C, C++, Python and/or MATLAB.
- Knowledge of deep learning frameworks would be a plus.
- A prior publication in one of the major conferences or journals in computer vision/machine learning is not necessary but would be very desirable.
Application process: For consideration, please send a resume, names and contact details of two references, transcripts for undergraduate and graduate studies, and a link to a Masters thesis (as well as relevant publications if any) to Eric.Granger-AT-etsmtl.ca.
Further information :
Eric Granger
http://www.etsmtl.ca/Unites-de-recherche/LIVIA
Simon Bacon
http://mbmc-cmcm.ca/member/simon-bacon/
Last modified: 2018-03-15 21:37:43