Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- CNRS
- Nature Careers
- Institut Pasteur
- Arts et Métiers Institute of Technology (ENSAM)
- CEA
- Grenoble INP - Institute of Engineering
- Inria, the French national research institute for the digital sciences
- University of Paris-Saclay
- University of Reims Champagne-Ardenne (URCA)
- Université Paris-Saclay (UPS)
- Université Paris-Saclay GS Mathématiques
- 1 more »
- « less
-
Field
-
problems of on-device learning for spintronic devices, proposing and impl menting technical solutions and communicating his scientific results Where to apply E-mail job-ref-waft5vlowa@emploi.beetween.com
-
(with openness to learning others): Computational protein design or molecular modeling Protein biochemistry / structural biology Cell biology and receptor signaling Good communication skills in English
-
particular NLP, statistical learning, machine learning, generative AI, and their major fields of application. Roles and responsibilities The applicant will join the team of the 3IA Côte d’Azur Institute and
-
algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation
-
, engineering, bioinformatics, machine learning, artificial intelligence) to support minimally invasive and targeted preventive and predictive medicine capable of limiting age-related functional disorders
-
by exploiting foundational machine-learning potentials such as MACE, SevenNet, or Orb-V3. The predictions will then be progressively refined and verified by DFT and, ultimately, tested experimentally
-
be highly interdisciplinary. Two different profiles are possible for this position: either a profile in engineering sciences or biomedical physics, with a strong desire to learn about microbiology
-
correlations or more innovative methods of multivariate analysis and we anticipate here an opportunity of using machine learning that could help in predicting properties or classifying sources. A last step will
-
technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in
-
-flexible technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their