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Machine Learning-Robotics

Éléments
Domaine
Machine Learning-Robotics
Domain - extra
Machine Learning models of Grids
Année
2010
Starting
October 2010
État
Closed
Sujet
Characterizing the spatiotemporal structure and dynamics of e-science social networks
Thesis advisor
GERMAIN-RENAUD Cécile
Co-advisors
Laboratory
LRI A&O
Collaborations
Abstract
A complex system consists of many interacting units, whose collective behavior cannot be explained from the behavior of the individual units alone. Complex dynamical networks are complex systems that can be represented with graphs dynamically evolving in time. Computational grids provide new natural examples of large-scale complex networks emerging from collective behavior. Moreover, computational grids feature multiple levels of interactions. An interesting question is thus whether these networks will exhibit properties similar to those of social networks, or original ones, which would be the specific signature of e-science. An operational question is the creation of generative models appropriate for forecasting future graph structure. The PhD will characterize the spatiotemporal structure of the graphs created 1) by co-access to files, and 2) by the job traffic.
Context
Objectives
Work program
Extra information
Sujet (en anglais)/Subject (in English) at http://www.lri.fr/~cecile/Stages/ThesisCNCGR.pdf
Le candidat peut être francophone ou anglophone.
Prerequisite
M2 d'informatique/Master in Computer Science

Détails
Télécharger TheseComplexNetworks.pdf
Expected funding
Institutional funding
Status of funding
Expected
Candidates
Utilisateur
cecile.germain-renaud
Créé
Lundi 22 février 2010 20:53:28 CET
dernière modif.
Vendredi 18 juin 2010 14:32:55 CEST

Fichiers joints

 filenamecrééhitsfilesize 
TheseComplexNetworks.pdf 22 Feb 2010 20:53275771.88 Kb


Ecole Doctorale Informatique Paris-Sud

Directrice
Nicole Bidoit
Assistante
Stéphanie Druetta
Conseiller aux thèses
Dominique Gouyou-Beauchamps
ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr