Deadline: April 30, 2013
Open to: Candidates holding (or expecting to receive) a master in mathematics, statistics, economics or related areas.
Remuneration: not specified
In the framework of the Interuniversity Attraction Pole (IAP) P7/06 StUDyS the Université libre de Bruxelles (ULB) announces a PhD position in statistics. The doctoral position in statistics is for 4 years, with a workload of 40 hours per week, starting on 1 October 2013.
More Information About The Project
Stochastic systems are influenced by internal characteristics of the system, but may as well depend on (interactions with) external factors, and may evolve over time and/or space. The main goal of this IAP-network is to develop statistical methods that are crucial for complete understanding of certain classes of complex dynamic systems, and to use these to answer challenging questions in focused applications.
Due to advances in technology, the complexity of the total data structure is often quite involved: different types of data, several sources of information on a same subsystem, few measurements on a large number of characteristics. Although more and/or diverse information is to be applauded for, consequently, extraction of the important information from it becomes a real challenge.
Common elements in many complex problems are: the complex interplays between various characteristics (of possibly very different conceptual nature), and the various layers of dynamics (time, space, …).
The analysis of complex stochastic systems based on advanced data structures, faces important challenges for statistics research, translated in the following main objectives:
- How to model and analyze dependence structures between random variables (of possibly different nature – such as real numbers, discrete values, functions, graphs) that themselves may vary with other covariates (also of possibly different nature); in time and/or space; and differently in the tails of the distributions?
- How to efficiently analyze data that exhibit several dynamics, e.g. in space and time. What are the most efficient statistical modeling techniques incorporating the various layers of dynamics?
- How to efficiently analyze data that are hierarchically structured (e.g. data with some cluster structure, network structure, missing data…)?
- How to account for the influence of non-observable variables in complex stochastic systems? What are the most efficient modeling techniques and associated statistical methods?
- How to select from a large, very large, or even huge set of measured characteristics (of possibly different nature) those that influence a variable of interest? What are the most efficient sparse so-called regularization techniques and how to select regularization parameters? How to draw conclusions from data sets that contain multiple sorts of information regarding the same complex (sub)system, and how to do data fusion?
They are 7 participating researchers from ULB (Mathematics Department and ECARES) working, among others, in the following areas:
- optimal inference in elliptical families,
- multivariate sign-and-rank techniques,
- depth-based methods,
- robustness methods,
- outlier detection,
- time series,
- factor models,
- functional data,
- monetary policy,
- business cycles,
- portfolio theory,
- inverse problems,
- sparsity-enforcing regularization theory,
- wavelets and applications,
- learning theory,
- minimum risk,
- multiscale methods.
The candidate should be interested in one or more of these areas and indicate this in his/her application. More information about the project can be found HERE.
Candidates holding (or expecting to receive) a master in mathematics, statistics, economics or related areas are invited to apply. The degree should be awarded before the starting date.
The application deadline is 30 April 2013. You are encouraged to apply as soon as possible since they might fill the position at an earlier stage. A full application consists of:
- a detailed CV,
- a statement of motivation,
- 2 reference letters,
- degree certificates and grade transcripts.