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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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based on the new data generated, incorporating key variables identified in (i), and use statistical and machine learning methodologies to ensure high predictive accuracy and robustness; iii) validation
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associate in the broad areas of high performance computing and machine learning. HighZ is focused on developing scalable high order methods, enhanced with surrogate models for subscale physics, for modeling
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machine learning (ML) approaches offer a powerful framework for modeling complex catalytic materials with near ab initio accuracy while enabling simulations at significantly larger spatial and temporal
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the project: Develop, train, and optimise deep learning models for wildlife species identification, classification, and segmentation using real-world datasets. Design and implement software modules to integrate
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required. The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative- genomics/) at Institut Pasteur, led by Laura Cantini, works at
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single‑cell omics, AI machine learning, and translational biology. The role involves collaboration with academic research group(s), with a strong focus on bridging advanced computational methods
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We are seeking creative and energetic candidates with strong experience in multimodal machine learning and human behavior analysis and modeling for a Pre-Doc Specialist position in the Robotics
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collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at
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Learning, or a closely related field. Strong understanding and demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands