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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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of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
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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
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to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8197-VALHER-223/Default.aspx Requirements Research FieldMathematicsEducation LevelPhD or equivalent LanguagesFRENCHLevelBasic Research
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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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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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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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- 4 Additional Information Eligibility criteria • Experience in computer modeling and programming • Knowledge of associative learning at both the neurobiological and psychological levels • Experience
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e.g., ultra-cold gases of bosonic or fermionic atoms, machine learning technologies and quantum computing. At the same time, we work in close connection with IJCLab experimentalists, particularly
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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