PamiTC Job Board - Posting Details

Title: PhD Fellowship in Computer Vision and Machine LearningPosted: June 11, 2014
Company/Institution: Ecole des Ponts ParisTech
Location: Paris, France


Ecole des Ponts ParisTech (, one of the most prestigious and selective French Grandes Ecoles as well as a prestigious institute member of ParisTech (Paris Institute of Technology), has an opening for a PhD Fellowship in the areas of computer vision and machine learning at the computer science and applied mathematics department.

We are searching for outstanding and highly motivated students who wish to perform cutting-edge research in one of a variety of research topics, including those related to probabilistic graphical models for image analysis, scene parsing and image annotation by use of multimodal training data, methods for analyzing dynamic image content (e.g., human pose estimation, optical flow matching), 3D scene understanding, large scale inference and learning methods, as well as problems from low-level vision such as deformable registration and image deconvolution.

Prospective applicants should have:

  • an exceptionally strong academic record with an excellent degree (M.Sc., M.Eng. or equivalent) in Computer Science, Mathematics, or a related field (e.g. Electrical Engineering)
  • demonstrable interest in computer vision and machine learning,
  • strong mathematical understanding, and
  • very good programming skills.

Any prior publications in first-tier computer vision/machine learning journals (TPAMI, IJCV, JMLR) and conferences (CVPR, ICCV, ECCV, ICML, NIPS) would be a very important plus.

Successful applicants will join an expanding research group consisting of more than 25 faculty members seeking scientific excellence in the area of learning and visual computing, and will be submerged into an "interdisciplinary" environment with over 75 permanent research scientists.

Deadline for applications: Open until filled

Application Instructions:

The application package consists of a motivation letter, full CV, names and contact details of two references, transcript of grades from under-graduate and graduate program, and a link to a Masters thesis (as well as to any relevant publications).

Applications should be submitted via electronic mail directly to Prof. Nikos Komodakis (