12 months post-doctoral position in applied probability
Stochastic models of interacting agents are used in many domains (caching systems, networks,...). The analysis of the model of n stochastic entities interacting with each others can be particularly difficult. The mean field approximation is a very effective technique to characterize the transient probability distribution or steady-state regime of such systems when the number of entities n grows very large. The idea of mean-field approximation is to replace a complex stochastic system by a simpler deterministic dynamical system. Our recent progress suggest that it is possible to extend these methods to study systems with a relatively small entities (n=10). The main mission of the post-doc will be to contribute to the development of theoretical and analytical tools on this subject, for example to focus on heavy-traffic regime and heterogeneous systems.