Applications are invited for a Ph.D. studentship at Sequential Learning Lab in INRIA Lille, France. The topic is time series prediction, and non-parametric statistical analysis of time series.
Position: 3 year fully funded position (contract). The position will start in September 2010. The deadline for applications will be mid March, but interested candidates are encouraged to contact me earlier. Environment: The PhD student will work with Daniil Ryabko http://daniil.ryabko.net , and with other members of the SequeL group http://sequel.futurs.inria.fr/ at INRIA Lille. SequeL is one of the most dynamic labs at INRIA, with over 20 researchers (including PhD students) working on both fundamental and practical aspects of sequential learning problems: from statistical learning, through reinforcement learning, to computer poker and Go players. INRIA is France's leading institution in Computer Science, with over 2800 scientists employed, of which around 250 in Lille. Lille is the capital of the north of France, a metropolis with 1 million inhabitants, with excellent train connection to Brussels (40 min), Paris (1h) and London (1.5h by train). Subject: The basic problem setup is as follows. 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. There are several learning and inference problems connected with it, of which the two most basic ones are: predicting the probabilities of the next observations, and testing hypotheses about the source (such as independence, homogeneity, etc.) To solve these problems, one has to consider models of the data. Different types of data require different models. The goal is to describe those probabilistic models under which successful learning is possible, for the inference problems considered: sequential prediction, and hypothesis testing. This would eventually lead to an automated modelling algorithms for sequential learning. The primary goal, however, is to establish a theoretical understanding of what is possible to learn, in the sequential problems of interest considered, under which assumptions. More precisely, a data source is a probability distribution on the set of all possible sequences of observations, and a model is a set of such probability distributions. We are interested in identifying the properties of models which ensure the existence of efficient algorithms that are successful (e.g. as predictors) given the model. Requirements: The successful candidate will have a MSc or equivalent degree in mathematics or computer science, with strong background in probability and statistics. Programming skills will be considered a plus. The working language in the lab is English. For further information please email daniil.ryabko at inria.fr , with subject -SeqPHD-, joining a CV and a description of interests. More information (including the application procedure) will soon be available through http://www.inria.fr/travailler/opportunites/doc.en.html _______________________________________________ uai mailing list [email protected] https://secure.engr.oregonstate.edu/mailman/listinfo/uai
