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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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postdoctoral fellowship at ENS Lyon in the field of machine learning. The position is part of the research project "Neural networks for homomorphic encryption", funded by Inria. Fully homomorphic encryption (FHE
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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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. In particular, he/she will be expected to :• Select and evaluate the most suitable approaches from the wide range of machine learning and computer vision methods available in the literature, with
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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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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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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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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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, Python, Bash). Good level on machine learning. Good level of written and oral English. Ease in a multidisciplinary environment, taste for teamwork, interpersonal skills. Scientific curiosity