PhD positions in theoretical machine learning at Inria Lille

The topic belongs to the areas of machine learning and (extremely) nonparametric statistics. The central theme of this topic is to explore which regularities are "learnable" from data. Specifically, this general question is considered for the problem of forecasting, that is, predicting the probabilities of future outcomes of a series of events given the past. The question to be addressed is: under which assumptions on the stochastic mechanism generating the data is it possible to find the solution to the problem? Various mathematical formulations of this problem are possible. The goal is to describe those probabilistic models under which successful learning is possible, for the inference problems considered: sequential forecasting. This would eventually lead to an automated modelling algorithms for sequential learning.
Background papers :
http://daniil.ryabko.net/ryabko10a.pdf
http://daniil.ryabko.net/ryabko11a.pdf
Job offer description
The primary goal, however, is to establish theoretical understanding of what is possible to learn, in the sequential problems of interest considered, under which assumptions.
Skills and profile
MSc in mathematics, statistics or computer science (should be obtained by the starting date). In case the degree is in computer science, strong mathematical background is a must.
Benefits
Possibility of French courses
Help for housing
Financial support from Inria to catering and transportation expenses
Scientific Resident card and help for visa
Catering service
Monthly salary after taxes: around 1580 ? the 1st two years and 1660 ? the 3rd year (social security included).
Additional information
Duration: 36 months
Starting date of the contract: 1st October 2015
Salary: 1958 ? the first two years and 2059 ? the third year.
Scientific contact: Daniil Ryabko, daniil.ryabko-AT-inria.fr
HR contact: Natacha Lecomte (natacha.lecomte-AT-inria.fr)