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, machine-learning model development, structural sensing and health monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful applicant must
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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based estimation from images and text metadata. Build and evaluate monocular depth pipelines
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from various sources, and collating them into datasets suitable for the training and evaluation of predictive models Training and evaluating predictive machine learning models on historical data from
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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physics-integrated machine learning models—to predict, analyze, engineer, and understand microbial community dynamics. Applications span precision medicine and built environment microbiomes, with a strong
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details of 2-3 references to laura.cantini@pasteur.fr For more information : https://research.pasteur.fr/en/team/machine-learning-for-integrative-genomics/
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DFT, beyond-DFT, and experimental techniques. We are also interested in developing both forward and inverse machine learning models to accelerate and optimize the design processes. We work in close