A position for a post-doctoral researcher in the area of robust estimation is available at IDSIA (Lugano, Switzerland, www.idsia.ch). Occupancy degree is 100%.
**Background In performing a Bayesian analysis concerning the unknown value of a parameter, one is required to express the prior beliefs about the parameter in the form of a prior distribution. However, prior information is often imprecise and cannot be expressed with a single prior distribution. In this case, an alternative approach is to express prior information in terms of a family consisting of all prior distributions that are deemed reasonable in representing one’s prior beliefs. Inferences should then carried out by considering the whole family of distributions. This approach is known as Bayesian robustness
- examples are contamination models, intervals of measures, density ratio classes, etc. In the case almost no prior information is available on the parameter, the family of distributions should be as large (i.e., as least-committal, or as weak) as possible in order to describe this state of prior ignorance. In this respect, the class of families known as near-ignorance priors has recently been the focus of much research: these are models that express a minimal state of beliefs a priori, thus representing a condition of prior ignorance on some random quantities of interest, while always leading to informative posterior inferences. Prior near-ignorance models can be regarded as an objective-minded approach to inference, in that prior beliefs are maximally weakened in favor of information coming from data. You can obtain further information and references from our website: http://ipg.idsia.ch/.
**Keywords: robust estimation, Bayesian robustness, set of distributions, imprecise probability, consistency of the posterior distribution, asymptotic analysis.
**Job description The goal of this project is first of all to develop new methods for robust estimation based on families of distributions with particular emphasis on near-ignorance models. Therefore the project will be mostly based initially on methodological developments at the theoretical level. The second part of the project will focus on the application of those methods to problems in which robustness is an issue such as, for instance, regression, filtering, time series analysis (e.g., robust Kalman filter) etc. The project is granted by the Swiss National Science Foundation and focuses on basic research.
**Requirements -have excellent mathematical skills, preferable in the area of probability and statistics, -have both a Master and a Ph.D. degree in mathematics or statistics or physics or engineering or related area, -have a strong publication record on topics related to statistical estimation, robust estimation, applied probability. -strong commitment to research and publication.
**We offer -a two-years position (degree of occupancy 100%), with possibility of prolongation; -international working environment (English is the official language); -funded travels in case of papers accepted by well-known international conferences.
**Application Applicants should submit the following documents, written in English: -curriculum vitae; -list of exams and grades obtained during the Bachelor and the Master of Science; -list of three references (with e-mail addresses); -brief statement on how their research interests fit the topics above (1-2 pages); -publication list and possibly links to the thesis.
Applications should be submitted by 31 August 2012 through the online form at the address:
www.supsi.ch/go/bando_robustestimation_researcher_IDSIA
Incomplete applications, or submitted to other addresses, or beyond the deadline will be not accepted.
For further information please refer to the official page for this position: http://www.supsi.ch/home/supsi/lavora-con-noi/2012-08-31.html or contact Alessio Benavoli, alessio(a)idsia.ch