19 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" Postdoctoral positions at CNRS in France
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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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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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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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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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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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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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). - 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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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