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The core component of our scientific agenda focuses on the development of statistical and probabilistic methods for the modeling and the optimization of complex systems. These systems require dynamic and stochastic mathematical representations with discrete and/or continuous variables. Their complexity poses genuine scientific challenges that can be addressed through complementary approaches and methodologies:

Modeling: design and analysis of realistic and tractable models for such complex real-life systems taking into account various probabilistic phenomena;

Estimation: developing theoretical and computational methods in order to estimate the parameters of the model and to evaluate the performance of the system;

Control: developing theoretical and numerical control tools to optimize the performance.

These three approaches are strongly connected and the most important feature of the team is to consider these topics as a whole. This enables the team to deal with real industrial problems in several contexts such as biology, production planning, trajectory generation and tracking, performance and reliability.

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