340 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at CNRS in France
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
the Swiss team led by Christophe Ballif (EPFL/CSEM). Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR9006-JEAGUI0-017/Default.aspx Requirements Research FieldEngineeringEducation LevelPhD
-
support machine learning applications for analyzing electron microscopy images of nanoalloys. Model interactions between nanoalloys and carbon substrates to reflect experimental conditions, incorporating
-
- Notions of solid state physics Group website: https://photonlattices.eu/ [1] R. Asapanna et al., Phys. Rev. Lett. 134, 256603 (2025). Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR8523
-
. 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
-
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
-
). - 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
-
: 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
-
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
-
of the project MESSENGER: « Can rock-powered Microbial EcoSyStEms provide valuable iNsiGhts into early life and its emERgence? » funded by the PEPR ORIGINS (https://pepr-origins.fr/en/ ) and integration
-
MICADO (the first light instrument of the Extremely Large Telescope). The project provides a collaborative network, engaging with leading experts in optics, astrophysics, and machine learning from