Job offer

ticated, making the automation of defense strategies a priority. One strategy

to address the challenges behind automation in such contexts is by consider-

ing an optimisation-centric approach. Estimating spectrum properties of large

scale graphs is a very hot topic in Computer, Network Science communities

and Machine Learning. In particular, the connectivity of large scale networks is

essential for the study of their performance and resilience against cyberattacks.

Especially, in the context of overlay networks and ad-hoc wireless networks

such understanding is critical.

Machine learning algorithms based on the Power iteration or the Rayleigh quo-

tient techniques have been used to estimate graph spectral properties. When

the graph is unknown, these techniques have been considered recently in

by coupling with a random walk exploration of the graph. In a cybersecurity

context, to know which node and link is a weak point of the network is very

important. Indeed, such vulnerability can lead to successfull cyberattacks on a

network. And if the network is large, it is very complex to identify such weak

point of the structure. This PhD project aims to answer this question and to

propose efficient algorithms based on recent Machine Learning techniques (con-

Les candidatures doivent avoir des compétences en mathématiques appliquées (modélisation stochastique, optimisation, graphes) et en informatique (python ou matlab) pour du calcul scientifique et simulations.Ideal candidates will have a strong background in applied mathematics (particularly in stochastic modeling, optimization, and graph theory) as well as experience in scientific programming (Python or MATLAB) for algorithm implementation and simulations.