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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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exploration strategies that go beyond traditional techniques such as linear programming or deterministic solvers. You will work on cutting-edge methods including: Bayesian optimization Surrogate modeling
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behaviour using computational approaches such as Bayesian program synthesis and inverse reinforcement learning. Investigate the diversity of motor commands that could implement observed behaviours and explore
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redefinition of behavioral features or pose challenges in their detection. The projects To address these challenges, we propose developing a Bayesian Program Synthesis (BPS) methodology for generating synthetic
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revisiting the study of these latent variable models with a Bayesian point of view and to understand how this evidence lower bound integrate implicit priors on the latent variables. Having a clear
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selection criterion in some extent. This strongly suggests revisiting the study of these latent variable models with a Bayesian point of view and to understand how this evidence lower bound integrate implicit