Summary Adaptive Markov Chain Monte Carlo (MCMC) methods are currently a very active field of research. MCMC methods are sampling methods, based on Markov Chains which are ergodic with respect to the target probability measure. The principle of adaptive methods is to optimize on the fly some design parameters of the algorithm with respect to a given criterion reflecting the sampler’s performance (optimize the acceptance rate, optimize an importance sampling function, etc…). A postdoctoral position is opened to work on the numerical analysis of adaptive MCMC methods: convergence, numerical efficiency, development and analysis of new algorithms. A particular emphasis will be given to applications in statistics and molecular dynamics. (Detailed description) Position funded by the French National Research Agency (ANR) through the 2009-2012 project ANR-08-BLAN-0218. The position will benefit from an interdisciplinary environment involving numerical analysts, statisticians and probabilists, and of strong interactions between the partners of the project ANR-08-BLAN-021
Required diploma PhD thesis in statistics or probability, with a competitive track record.
Required skills experience in MCMC methods and their mathematical analysis.
Deadline for applications : September 2010. Applications must include : a detailed CV with a description of realized projects a motivation letter a summary of the thesis 2 or 3 recommendation letters preferred starting dates and duration and must be sent to Gersende FORT ([email protected]) in pdf format; or by standard mail to : Gersende FORT (LTCI, 46 rue Barrault, 75 634 Paris Cedex 13, Paris, France).
Duration : 11 months.
Location : Paris.