This internship aims at developing a hybrid methodology that unifies model-based observers (affine observers, high-gain observers, Unknown Input Observers (UIOs), interval observers, etc.) with machine learning methods (generative models, latent-state networks, hybrid architectures) in order to provide robust, explainable, and uncertainty-quantified state estimation. Expected areas of expertise:Strong background and interest in the interaction between control theory, applied mathematics, and artificial intelligence. Programming skills in MATLAB and/or Python are required.Application:Curriculum vitae, motivation letter, and academic transcripts (M2 and, if applicable, previous degrees).Contacts: Thach Ngoc Dinh, Associate Professor (ngoc-thach.dinh@lecnam.net)Jae Yun Jun Kim, Associate Professor (jae-yun.jun-kim@ece.fr)