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survival of lung cancer patient, notably those targeting different driver mutations such as EGFR, ALK, KRAS, and ROS1. The treatments targeting KRAS G12C mutation, one of the most frequent genomic alteration
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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
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on therapeutic targets. Modeling macromolecular complexes in silico. Environment. The AFMB is located on the Luminy campus of Aix-Marseille University (amU), at the heart of the Calanques National Park . It is
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Background: The nuclear oncology team has a long-lasting expertise in the development of targeted alpha therapy and is internationally recognized for its research on astatine-211 labeling chemistry
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computational models to map and analyze interactions within biological networks, thereby identifying key pathways and targets for therapeutic intervention. -Simulations and Virtual Drug Screenings: Aiding in drug