The chaire “Economie et gestion des nouvelles données” is recruiting a talented postdoc specialized in large scale computing and data processing. The targeted applications include machine learning, imaging sciences and finance. This is a unique opportunity to join a newly created research group between the best Parisian labs in applied mathematics and computer science (Paris Dauphine, INRIA, ENS Ulm, Ecole Polytechnique and ENSAE). The proposed position consists in working in the research of large scale data processing methods, and applying these methods on real life problems.
The successful candidate will integrate the Sierra INRIA team (http://www.di.ens.fr/sierra/) located at the new INRIA Paris center located in downtown Paris. He will benefit from a very stimulating working environment and all required computing resources. He will work in close interaction with the 4 research labs of the chaire, and will also have interactions with industrial partners.
A non exhaustive list of methods that are currently investigated by researchers of the group, and that will play a key role in the computational framework developed by the recruited post-doc, includes :
* Large scale non-smooth optimization methods (proximal schemes, interior points, optimization on manifolds).
* Distributed optimization methods (asynchronous stochastic gradient optimization).
* Machine learning problems (kernelized methods, Lasso, collaborative filtering, deep learning, learning for graphs, learning for time dependent systems), with a particular focus on large scale problems and stochastic methods.
* Asynchronous parallel optimization methods.
* Imaging problems (compressed sensing, superresolution).
* Hyperparameter optimization.
## Candidate profile
The candidate should have a good background in computer science with various programming environments (e.g. Python and/or Matlab and/or Java/Scala) and knowledge of high performance computing methods (e.g. parallelization, GPU, cloud computing). He/she should adhere to the open source philosophy and possibly be able to interact with the relevant communities (e.g. Python, scikit-learn, Julia project, etc.). Typical curriculum includes PhD in computer science, applied mathematics, statistics or related fields.
## Application proces
Send a resume and a motivation letter to:
Alexandre d’Aspremont <firstname.lastname@example.org>, Robin Ryder <email@example.com>, Fabian Pedregosa <firstname.lastname@example.org>
For any questions please contact me at email@example.com .