Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- CNRS
- Nature Careers
- Inria, the French national research institute for the digital sciences
- Institut Pasteur
- Aix-Marseille Université
- CEA
- The American University of Paris
- Université Grenoble Alpes
- Université de Technologie de Belfort-Montbéliard
- Arts et Métiers Institute of Technology (ENSAM)
- BRGM
- Consortium Virome@tlas
- Ecole Centrale de Lyon
- Ecole polytechnique
- FEMTO-ST institute
- French National Research Institute for Sustainable Development
- ICMMO
- INSERM U1183
- IRISA
- Institut Curie - Research Center
- Institut of Mathematics of Marseille
- Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique,
- Observatoire de la Côte d'Azur
- University of Paris-Saclay
- Université Côte d'Azur
- Université Paris-Saclay (UPS)
- Université Paris-Saclay GS Mathématiques
- Université de Caen Normandie
- École Normale Supéireure
- École nationale des ponts et chaussées
- 20 more »
- « less
-
Field
-
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
-
(formulation, algorithms, applications in structural mechanics), HPC computing, reduced-order modelling, machine learning, Vibrations and structural dynamics, architected materials, Additive manufacturing
-
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
-
machine learning approaches to integrate single cell and spatial analysis in order to identify molecular signatures and pathways underlying radiation-induced effects. Collaboration: Work in close
-
for such applications. To respond to these challenges, this project aims to investigate automated decision making based on machine learning. The candidate (H/F) will propose and validate centralized as
-
, CNRS, I3S, Sophia-Antipolis, France) Collaboration: Luca Calatroni (Luca.calatroni@unige.it), Machine learning Genoa Center, Italy. Context and Post-doc objectives Conventional optical microscopy
-
Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | 18 days ago
(denoising, Mueller matrix calculation/decomposition) and AI-based diagnostic algorithms using machine/deep learning. The primary challenge will be to deliver practitioner-relevant cervical images in under 0.5
-
visualization. Experience with GWAS, Bayesian modelling, and/or machine learning applied to biological data. Strong programming skills (R, Python) and ability to manage large-scale -omics datasets. Good
-
). An overview of recent multi-view clustering. Neurocomputing, 402, 148-161. 7. Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021). Machine learning for perturbational single-cell omics. Cell Systems, 12