Sort by
Refine Your Search
-
Listed
-
Employer
- Inria, the French national research institute for the digital sciences
- CNRS
- Ecole Centrale de Lyon
- Centrale Supelec
- European Synchrotron Radiation Facility
- IFP Energies nouvelles (IFPEN)
- INRIA
- Institut Pasteur
- LEM3
- Nantes Université
- Universite de Montpellier
- Université Gustave Eiffel
- Université Toulouse Capitole
- Université côte d'azur
- cnrs
- 5 more »
- « less
-
Field
-
stability analysis and control, machine learning, dimensionality reduction and high-performance computing. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UPR3346-NADMAA-159/Default.aspx
-
Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 16 minutes ago
Website https://jobs.inria.fr/public/classic/en/offres/2026-09928 Requirements Skills/Qualifications Profile: - The candidate is completing a Master's or engineering’s degree in Computer Vision, Electrical
-
differential equation models of bacterial persistence. A particular challenge, both for simulation and for machine learning, lies in the high dimensionality of these equations, which causes grid-based numerical
-
Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 16 minutes ago
Computer Science, Machine Learning, Bioinformatics, Computational Biology, or related fields. Strong experience in deep learning, ideally with PyTorch. Proven experience with graph neural networks, geometric deep
-
, SIAM Review, 60(3):550–591 (2018). [4] Diederik P Kingma and Max Welling, Auto-Encoding Variational Bayes, International Conference on Learning Representations (ICLR) 2014 ArXiv. http://arxiv.org/abs
-
plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
-
the "Machine Learning and Gene Regulation" team led by William Ritchie, specializing in bioinformatics and post-transcriptional regulation. The scientific environment at the IGH — international seminars, journal
-
Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 17 days ago
leverage machine learning techniques to bypass IO bottlenecks in the context of physics simulation on high-performance computing (HPC) clusters. This work is thus placed in a broader ``Machine Learning for
-
plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
-
training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical