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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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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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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes
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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database
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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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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 9 days ago
skills Specific Requirements The candidate must hold a PhD in machine learning and have strong mathematical skills. Knowledge of bandit models is a plus. LanguagesFRENCHLevelBasic LanguagesENGLISHLevelGood
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) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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of results at conferences - interaction with team members and international collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning