Hi all,
apologies for the off-topic email but my lab has an opening that might be
of interest of people within this list. The candidate will be strongly
encouraged to open source his contributions and/or interact with this
project.
Best,
Fabian
-----------
Position as data scientist
Chaire "Economie et gestion des nouvelles données"
● Location: within one of the lab of the chaire (Paris-Dauphine, ENS Ulm,
Ecole Polytechnique or ENSAE).
● Duration: 1 year renewable at least once.
● Salary: to be discussed depending on the applicant’s profile.
● Start: as early as June 2015.
● Application process: send a resume and a motivation letter to:
Alexandre d'Aspremont <aspre...@ens.fr>, Stephane Gaiffas <
stephane.gaif...@cmap.polytechnique.fr>, Robin Ryder <
ry...@ceremade.dauphine.fr>
*Job description*
The chaire "Economie et gestion des nouvelles données" is recruiting a
talented young engineer 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, ENS Ulm, Ecole Polytechnique and ENSAE)
working hand in hand with major industrial companies (AXA Global Direct,
Havas, BNP Paribas, Warner Bros.). The proposed position consists in
helping researchers of the group to develop and implement large scale data
processing methods, and applying these methods on real life problems in
collaboration with the 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 engineer, includes :
● Large scale non-smooth optimization methods (proximal schemes, interior
points, optimization on manifolds).
● 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.
● Imaging problems (compressed sensing, superresolution).
● Approximate Bayesian Computation (ABC) methods.
● Particle and Sequential Monte Carlo methods
*Candidate profile*
The candidate should have a good background in computer science with
various programming environments (e.g. Python, Matlab, C++) 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. scikit-learn
project). Typical curriculum includes engineering school or Master studies
in computer science / applied maths / physics, and possibly a PhD (not
required).
*Working environment*
The recruited engineer will work within one of the labs of the chaire. 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 regular meetings with the industrial
partners.
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