324 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" positions at CNRS in France
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
part of the research theme 'Planets and Moons', and will be integrated within the ERC - IceFloods (https://lpg-umr6112.fr/en/erc-icefloods/ ). This thesis will aim to characterize the contribution of ice
-
: Marine Biodiversity and ecosystem functioning across spatial, temporal, and human scales”. The overall aim of the project is to acquire knowledge of the principles governing the structure, dynamics
-
motivated to acquire new skills. Candidates must be fluent in English and/or French with scientific writing skills. The doctoral contract will take place at the CRISMAT laboratory (https://crismat.cnrs.fr
-
” team. Website: https://cermav.cnrs.fr/en/equipe/physico-chemistry-and-self-assembly-of… Team Leader: R. Borsali The successful candidate will be responsible for synthesizing glycopolymers based
-
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
-
- 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
-
congested architectures. For more information, visit my professional website: https://iscr.univ-rennes.fr/daniel-muller Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR6226-DANMUL-005
-
support machine learning applications for analyzing electron microscopy images of nanoalloys. Model interactions between nanoalloys and carbon substrates to reflect experimental conditions, incorporating
-
. 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
-
). - 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