307 machine-learning "https:" "https:" "https:" "https:" "U.S" "U.S" positions at CNRS
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: interferons alpha, lambda, cross-presentation https://institutcochin.fr/projet-6-cellules-dendritiques-contre-vih-int… . The group is expert in studying interactions between human DC and HIV-infected cell
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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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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Office equipped with a computer station and common
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), Ryoji Shinya (Meiji University, Japan). Background: Mignerot et al. 2024 https://doi.org/10.7554/eLife.88253.2 Kanzaki et al. 2021 https://doi.org/10.1038/s41598-021-95863-1 Our team (http://ibv.unice.fr
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that they can integrate it into their large-scale quantum computer system engineering models. SKILLS. Candidates must have a high-quality background in quantum information or quantum physics, and an
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ultrasound, Laboratoire d'imagerie Biomedical, LIB , https://www.lib.upmc.fr/ ) and nanoparticle engineering ( PHENIX Laboratory https://phenix.cnrs.fr/ ). The LIB is located in the Centre de Recherche des
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new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably
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impact measurements, AFM imaging, and AFM-SECM experiments. The SEEAFM project will be developed within the Electrochemistry group of the IMF team at the CEISAM laboratory. Where to apply Website https