Sort by
Refine Your Search
-
Listed
-
Employer
- CNRS
- Nature Careers
- Inria, the French national research institute for the digital sciences
- Ecole Normale Supérieure de Lyon
- Institut Pasteur
- Universite de Montpellier
- CEA-Saclay
- IFP Energies nouvelles (IFPEN)
- IMT - Institut Mines-Télécom
- IMT Atlantique
- IMT MINES ALES
- IMT Mines Ales
- INSA Strasbourg
- INSTITUT MAX VON LAUE - PAUL LANGEVIN
- Télécom Paris
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE
- University of Strasbourg
- Université Grenoble Alpes
- Université Savoie Mont Blanc
- École nationale des ponts et chaussées
- 10 more »
- « less
-
Field
-
collaboration between the Exa-SofT and the Exa-DI projects and better support multi-linear algebra and tensor contractions in exascale CSE applications and Machine Learning. As part of the collaborative process
-
computer scientist with experience in bioinformatics, solid programming skills and knowledge in 3D protein structures. Machine learning skills and knowledge of Web development are a plus. Good interpersonal
-
difficult to couple with basin simulators. Geochemical metamodels, particularly those based on machine learning, can significantly reduce computation times while maintaining physico-chemical consistency
-
/chercheur-fh-en-simulation-des-deformations-des-tis… Requirements Research FieldEngineering » Computer engineeringEducation LevelPhD or equivalent Research FieldBiological sciences » Biological
-
researchers with ample experience in MEG/EEG data analysis, BCIs, signal processing, deep learning for brain imaging analysis, biomedical statistics, dynamical systems and research on motor control. The lab has
-
project will have additional specific requirements that candidates have to fulfill, be sure to check what these are before you apply. As a research fellow at the AMBER programme, you will acquire
-
Machine/Deep learning and classification Knowledge of the Linux operating system for using a computing cluster Interest in transdisciplinarity and teamwork Autonomy and scientific rigor Website
-
on the plants Arabidopsis thaliana will generate maps of depolarization, retardance, dichroism, and optical axis azimuth, which will feed machine learning models developed by the project partners to identify
-
of the project is to exploit such data to develop generative models for aptamer design. The candidate is expected to have a strong background in machine learning and statistical physics, with a real interest for