Sort by
Refine Your Search
-
Listed
-
Employer
- CNRS
- Nature Careers
- Inria, the French national research institute for the digital sciences
- Télécom Paris
- CEA-Saclay
- IMT - Institut Mines-Télécom
- IMT Atlantique
- IMT MINES ALES
- IMT Nord Europe
- INSERM U1028
- Institut Curie - Research Center
- Institut Pasteur
- UNIVERSITE ANGERS
- Université Grenoble Alpes
- Université côte d'azur
- École nationale des ponts et chaussées
- 6 more »
- « less
-
Field
-
. 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
-
these autonomy and self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed
-
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
-
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
-
the project team, you will ensure the simulation of drone missions using state-of-the-art tools for AI learning and demonstration. You will be responsible for producing training data for vision models and
-
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
-
of the project is to design, model and simulate neural networks based on magnetic skyrmion nucleation and propagation. The second objective is to fabricate these hardware neural networks, characterize
-
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
-
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