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computational reconstruction methods based on AI (deep learning) and/or compressed sensing. The envisioned imaging system will be based on a hybrid open-top light sheet microscope recently implemented in our lab
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/machine-learning-for-integrative-genomics/ The HUB : https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/ Degree : PhD in computer science, computational biology, bioinformatics
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Python-based frameworks or a web application) enabling image upload, AI-based analysis, and visualization of bacterial phenotypes. Perform image analysis using existing deep learning algorithms developed
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Research Engineer/Postdoctoral Position Decision and Bayesian Computation (DBC) – Epiméthée (EPI) Laboratory Institut Pasteur, Paris | 25 rue du Docteur Roux, 75015 Paris Position Overview We
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significant computational component. We strongly recommend a background in machine learning and coding. Applicants with a background in areas such as computational neuroscience, reinforcement learning, or deep
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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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, including Titan Krios and Glacios microscopes, a fully equipped crystallography platform, advanced computing clusters, proteomics and BSL-2/3 imaging facilities. The institute provides numerous training