[visionlist] Deep Learning Architectures Face Recognition in Video Surveillance

Jose Dolz jose.dolz.upv at gmail.com
Fri Apr 7 09:17:56 -04 2017


Applications are invited for a funded PhD position in machine learning at
the École de technologie supérieure (ETS), U. of Quebec, Montreal, Canada. The
candidate will work under the supervision of Profs. Eric Granger and Ismail
Ben Ayed in the Laboratory for Imaging, Vision and Artificial Intelligence
(LIVIA, see link below). The position is available immediately after the
candidate passes ETS application requirements. Financial support is
available for the project’s duration (maximum of 3-4 years).

We are looking for highly motivated doctoral students, who are interested
in performing cutting-edge research in spatio-temporal face recognition for
video surveillance applications, with a particular focus on deep learning
(e.g, CNN and LSTM) architectures, information fusion and domain
adaptation.

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, neural
   networks, computer vision and face recognition;


   -

   A good mathematical background;


   -

   Good programming skills in languages such as C, C++, Python and/or
   MATLAB.

A prior publication in one of the major conferences or journals in computer
vision/machine learning is not necessary but would be a 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 (Eric.Granger at etsmtl.ca)

Ben Ayed, Ismail (Ismail.BenAyed at etsmtl.ca)

Laboratory for Imaging, Vision and Artificial Intelligence (LIVIA):

http://www.etsmtl.ca/Unites-de-recherche/LIVIA/accueil?lang=en-CA

-- 
*Jose Dolz*
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://visionscience.com/pipermail/visionlist_visionscience.com/attachments/20170407/9705027e/attachment-0001.html>


More information about the visionlist mailing list