The post-doctorate corresponds to the context of flight tests of an instrumented vehicle (space shuttle, capsule or probe) which enters into the atmosphere. The aim is to reconstruct, from measurements (inertial unit, radar, meteorological balloon, etc.), the trajectory and various quantities of interest, in order to better understand the physical phenomena and to validate the predictive models. We focus on Bayesian statistics, associated with Markov chain Monte Carlo (MCMC) methods. The post-doctoral fellow will develop and extend the proposed approach and will benefit from a scientific collaboration with Audrey Giremus, professor at the University of Bordeaux and specialist in the field. We will in particular try to increase the performance of high dimensional sampling. Special attention will be paid to the machine learning issue of the exploitation of an aerological database. The final objective will consist in developping an evolving software prototype dedicated to the post-flight analysis of flight tests, that exploits the various sources of information. The evaluations will be based on simulated and real data, with comparison to existing tools. The collaboration work will lead to scientific communications and publications.