Sort by
Refine Your Search
-
Listed
-
Employer
- CNRS
- Nature Careers
- Inria, the French national research institute for the digital sciences
- IMT Atlantique
- Institut Pasteur
- Ecole Normale Supérieure de Lyon
- Télécom Paris
- Université Grenoble Alpes
- CEA-Saclay
- IMT - Institut Mines-Télécom
- IMT Nord Europe
- INSERM U1028
- INSTITUT MAX VON LAUE - PAUL LANGEVIN
- Institut Curie - Research Center
- UNIVERSITE ANGERS
- UNIVERSITE D'ORLEANS
- Université Savoie Mont Blanc
- Université côte d'azur
- 8 more »
- « less
-
Field
-
. 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
-
molecular dynamics simulations of modified nucleosomes - analyze the large data set obtained using various analysis tools, from visualization to automation using machine learning tools - perform QM/MM
-
technologies (fiber-optic sensors, DIC), and computer science (machine learning tools) in collaboration with de department of Physics. The aim of the BriCE project is to develop a novel bridge monitoring
-
. Skills in computational modelling or machine learning applied to brain signals are an asset. We are looking for a highly motivated, rigorous and curious researcher who is ready to invest themselves in a
-
applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department, e.g. seminars, workshops and schools organised by the members
-
lie at the crossroads of multiple disciplines and involve expertise in optics, electronics, image and data processing (including machine learning), photophysics, chemistry and biology. The position is
-
expertise or interdisciplinary experience is a major asset. Scientific skills - In-depth knowledge of teaching strategies, learning models, and educational technology. - Proficiency in the psychology of well
-
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
-
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
-
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