116 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions in France
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3 Mar 2026 Job Information Organisation/Company UNIVERSITE LE HAVRE NORMANDIE Research Field Computer science » Computer systems Computer science » Database management Computer science » Modelling
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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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France 91120, France [map ] Subject Area: Applied Mathematics - machine learning and applied probability Appl Deadline: 2026/03/24 03:59 AM UnitedKingdomTime (posted 2026/02/03 05:00 AM UnitedKingdomTime
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training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical
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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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responsibility of developing predictive tools based on machine learning for the analysis and interpretation of Raman vibrational spectra applied to battery materials. The successful candidate will design and
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collaboration with L. Bonati at IIT Genova, who developed the library mlcolvar, https://github.com/luigibonati/mlcolvar ). 2) Compare the data-science dimensional reduction approaches above, with machine learning
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at the crossroads of AI, machine learning, bioinformatics and genomics, and in developing new methods rather than just applying existing ones, we'd like to hear from you. Website for additional job details https
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support machine learning applications for analyzing electron microscopy images of nanoalloys. Model interactions between nanoalloys and carbon substrates to reflect experimental conditions, incorporating
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problems, statistical learning and machine learning (machine learning, deep learning) - Knowledge of associated software development tools and environments: Python, PyTorch, Scikit-learn, Jax, Julia