2018-00746 - [NEO] Post-Doctoral Researcher in Machine Learning
Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD de la fonction publique

Contrat renouvelable : Oui

Niveau de diplôme exigé : Bac + 5 ou équivalent

Fonction : Post-Doctorant

Niveau d'expérience souhaité : Jeune diplômé

A propos du centre ou de la direction fonctionnelle

The Inria Sophia Antipolis - Méditerranée center counts 37 research teams and 9 support departments. The center's staff (about 600 people including 400 Inria employees) is composed of scientists of different nationalities (250 foreigners of 50 nationalities), engineers, technicians and administrators. 1/3 of the staff are civil servants, the others are contractual. The majority of the research teams at the center are located in Sophia Antipolis and Nice in the Alpes-Maritimes. Six teams are based in Montpellier and a team is hosted by the computer science department of the University of Bologna in Italy. The Center is a member of the University and Institution Community (ComUE) "Université Côte d'Azur (UCA)".

Contexte et atouts du poste

PostDoc Openning at Inria Sophia Antipolis, France
at Team NEO
under the supervision of Prof. K. Avrachenkov
This position is within the framework of the joint laboratory Inria - Nokia Bell Labs.

Mission confiée

Topic: Statistical Physics Methods for Distributed Machine Learning

Detailed description: Over the last few years, research in computer science
has shifted focus to machine learning methods for the analysis of increasingly
large amounts of user data.  As the research community has sought to optimize
the methods for sparse data and high-dimensional data, more recently new problems
have emerged, particularly from a networking perspective that had remained in the
periphery. These new directions go beyond sparsity of data and concern the
distributed nature of data sources as well as the computation itself.

We feel that statistical physics methods such as Gibbs sampling [3] and Generalized
Potts Model [2,4] are particularly well suited to design light complexity, distributed
machine learning methods for the tasks of unsupervised and semi-supervised
learning [1].

The candidate is expected to work on both theoretical and practical aspects of the
topic. We intend to employ both mean-field methods [5] and replica method [6] for
the analysis of the statistical physics based machine learning algorithms.


[1] Avrachenkov, K., Goncalves, P., Mishenin, A., and Sokol, M.
Generalized optimization framework for graph-based semi-supervised learning.
In Proceedings of SDM 2012.

[2] Blatt, M., Wiseman, S. and Domany, E.
Clustering data through an analogy to the Potts model.
Advances in Neural Information Processing Systems, pp.416-422, 1996.

[3] Bremaud, P. Markov chains: Gibbs fields, Monte Carlo simulation, and queues.
Springer, 2009.

[4] Eaton, E. and Mansbach, R.
A Spin-Glass Model for Semi-Supervised Community Detection.
In Proceedings of AAAI 2012.

[5] Nishimori, H.
Statistical physics of spin glasses and information processing: An introduction.
Clarendon Press, 2001.

[6] Mezard, M. and Montanari, A., 2009. Information, physics, and computation.
Oxford University Press.

Principales activités


The main activity is writing journal and conference papers with possiblity of patenting algorithms.




PhD in Mathematics, Computer Science or Physics is required.

Good knowledge of probability or statistical physics.

Knowledge of machine learning is a plus; knowledge of python is another plus.



Avantages sociaux

  • Subsidised catering service
  • Partially-reimbursed public transport
  • Social security
  • Paid leave
  • Flexible working hours
  • Sports facilities


Gross Salary: 2653€ brutto per month