2019-01636 - Post-Doctoral Research Visit F/M Asynchronous distributed algorithms for information retrieval and 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 : Thèse ou équivalent

Fonction : Post-Doctorant

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

This position is within the framework of the joint laboratory
Inria - Qwant Search Engine.

 

Mission confiée

 

Resume:


Many tasks in information retrieval (IR) and machine learning (ML) require operation
with very large volumes of data, often distributed across distant databases.
This calls for development of asynchronous distributed algorithms. Let us
mention just a few typical tasks in IR and LM that require distributed
approaches to process the data: PageRank [1], databases update and synchronization [2],  
distributed (federated) machine learning [3]. Most existing distributed approaches
(see e.g., [4-7]) for the mentioned tasks are either not asynchronous or have slow
convergence. Thus, we aim to design, to analyse and to test rapidly convergent asynchronous
distributed approaches.

Related references:

[1] L. Page, S. Brin, R. Motwani and T. Winograd, 1999.
The PageRank citation ranking: Bringing order to the web.
Stanford InfoLab Research Report.

[2] J. Cho, H. Garcia-Molina, 2000.
Synchronizing a database to improve freshness.
ACM Sigmod Record 29(2), 117-128.

[3] J. Konecny, H.B. McMahan, F.X. Yu, P. Richtarik, A.T. Suresh and D. Bacon, 2016.
Federated learning: Strategies for improving communication efficiency.
ArXiv preprint arXiv:1610.05492.

[4] M. Li, et. al. (2014).
Scaling distributed machine learning with the parameter server.
In 11th USENIX Symposium on Operating Systems Design and Implementation (pp. 583-598).

[5] S. Abiteboul, M. Preda and G. Cobena, 2003.
Adaptive on-line page importance computation.
In Proceedings of the 12th international conference on World Wide Web (pp. 280-290). ACM.

[6] K. Avrachenkov, N. Litvak, D. Nemirovsky and N. Osipova, 2007.
Monte Carlo methods in PageRank computation: When one iteration is sufficient.
SIAM Journal on Numerical Analysis, 45(2), pp.890-904.

[7] K. Avrachenkov, V.S. Borkar and K. Saboo, 2016.
Distributed and Asynchronous Methods for Semi-supervised Learning.
In Algorithms and Models for the Web Graph: 13th International Workshop, WAW 2016,
Montreal, QC, Canada, December 14–15, 2016, pp.34-46.

 

Principales activités

The main activity is to design and to test algorithms along with presenting the algorithms
in journal and conference papers with possiblity of patenting.

Compétences

PhD in Mathematics, Computer Science or Electrical Engineering;
Solid background in Linear Algebra, Optimization, Probability and Statistics is required;
Knowledge of Python as working programming language is another requirement.

Experience in machine learning or information retrieval is a plus.

Avantages

  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
  • Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage

Rémunération

Gross salary : 2632€ monthly