Объявления о PhD, Postdoc, Schools, Confs etc. - Public forum of MSU united student networks

[Мопед не мой. Владелец мопеда может научить хорошему. А желающих - и плохому.]

Applications are invited for a Ph.D. studentship at Sequential Learning lab in INRIA Lille, France.

The candidate will work with Daniil Ryabko on one of the following two topics (please select one when applying):

- Topic1

Learning representations for sequential and structured data

A variety of modern applications face the situations where a large amount of data has to be analysed, without much feedback on the quality of the analysis. Particularly challenging applications are presented by the Web and social network data, but the general problem permeates a host of applications, including network traffic analysis, medical and biological data. However, statistical models underlying modern analysis tools are usually fairly simple: independent identically distributed random variables, finite-state Markov chains, small parametric families, etc. Thus, the problem arises of finding a way of representing complicated data in a simple form, making the data amenable to statistical analysis. Such representations often have to be found in an automated fashion, with little or no feedback.

The focus of this project is to develop and analyse a mathematically sound framework to representation learning in problems involving sequential and structured data, as well as to develop algorithms that would work in simulation and on real applications. Several problems dealing with different kind of data can be considered: the problem of automated control, the sequential learning problem, learning on large (or infinite) graphs, and so on. The concrete choice, as well as the balance between theory, algorithms and implementations, will largely depend on the interests and background of the applicant.

- Topic 2
Learnability in sampling-based inference problems

There is an unknown stochastic source of data, generating observations in a sequential fashion. The data can be anything from stock market observations, to DNA sequences, to behavioural sequences. A more general problem is when the data is not a sequence but a large (potentially infinite) graph, such as a social network. There are several learning and inference problems connected with it, of which the two most basic ones are: predicting the probabilities of the future observations, and testing hypotheses about the source (such as independence, homogeneity, hypotheses about its structure, etc.) To solve these problems, one has to consider models of the data. Different types of data require different models.

The goal of this research project is to describe those probabilistic models under which successful learning is possible, for the inference problems considered: prediction and hypothesis testing. The primary goal is to establish a theoretical understanding of what is possible to learn, in the learning problems of interest considered, under which assumptions. The focus is on sequential data, but more general structures (such as graphs) can also be considered.

This research topic is mainly mathematical.

--- Requirements:

The successful applicant will have a Master's (or equivalent) degree in Computer Science, Statistics, or related fields.
Strong mathematical skills are a must. Programming skills will be considered as a plus. The working language of the group is English (the knowledge of French is not required).

--- Benefits:

- Duration: 36 months - starting date of the contract : October 2013, 15th
- Salary: 1957.54 Euros the first two years and 2058.84 Euros the third year
- Monthly salary after taxes: around 1597.11 Euros the first two years and 1679,76 Euros the 3rd year (benefits included)
- Possibility of French courses
- Help for housing
- Participation for transportation
- Scientific Resident card and help for husband/wife visa

--- About INRIA and Team SequeL:

SequeL (https://sequel.lille.inria.fr ) is one of the largest and most dynamic teams at INRIA (http://www.inria.fr ), with over 25 researchers and Ph.D. students working on several aspects of machine learning from theory to application, including statistical learning and sequential decision-making. The SequeL team is involved in national and European research projects and has collaboration with international research groups. This allows the Ph.D. candidate to collaborate with leading researchers in the field at top universities in Europe and North America such as University College of London (UCL), University of Alberta, and McGill University. Lille is the capital of the north of France, a metropolis with over one million inhabitants, and with excellent train connection to Brussels (30min), Paris (1h) and London (1h30).

---Application

For further information please email daniil.ryabko [at] inria.fr ,
with subject -PHD-, joining a letter of motivation including a description of the research interests, a CV and a description of interests, name and contact information of up to three references, and other documents that may be relevant.

Начать обсуждение