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with Simulation-based Inference ), funded by the Multidisciplinary Institute for Artificial Intelligence (MIAI) through an AIforScience Research Chair. ADACSI will develop a multi-probe simulation-based
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Théorique (LAPTh, CNRS/Univ. Savoie Mont Blanc) invites expressions of interest for one or more postdoctoral researcher positions focused on advancing simulation-based Inference (SBI) frameworks for modern
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determines observables of the replication program such as the Mean Replication Timing (MRT) and the Replication Fork Directionality (RFD) profiles. We proposed a strategy to train a neural network to infer
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, integrating statistical inference, machine learning, and population genetics. We will develop advanced computational methods to characterize the functioning of T- and B-cell repertoires. The goal is to build
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to accelerate formulation discovery. Experimental data will be organised into a comprehensive database and analysed using statistical learning and Bayesian optimisation, establishing a closed-loop framework
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environmental metadata with metagenome-inferred phylogenetic diversity, functional predictions and other genetic components (e.g. selfish genetic elements facilitating horizontal gene transfer composing