Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- CNRS
- Inria, the French national research institute for the digital sciences
- Nature Careers
- Ecole Centrale de Lyon
- Centrale Supelec
- CentraleSupélec Rennes campus
- European Synchrotron Radiation Facility
- IFP Energies nouvelles (IFPEN)
- INRIA
- Institut Pasteur
- Institut polytechnique UniLaSalle
- LEM3
- Nantes Université
- Universite de Montpellier
- Université Gustave Eiffel
- Université Toulouse Capitole
- Université côte d'azur
- cnrs
- 8 more »
- « less
-
Field
-
or other large-scale biological data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, focusing on single-cell
-
, including machine learning and language technologies, for the integration and analysis of clinical, advanced data harmonisation, and next generation research infrastructures. You will contribute to research
-
(graduated or close to graduation) in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, Applied Mathematics, or related fields. Scientific curiosity and creative thinking
-
candidate will hold a PhD in geosciences, applied machine learning, data assimilation, or applied mathematics. The selection will be based on the following scientific and technical criteria: Experience in
-
perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon these pNTA results
-
new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably
-
Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
. Picchini. Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings. Transactions on Machine Learning Research, 2024 Kugler, F. Forbes, and S. Douté. Fast
-
democratization of approaches using artificial intelligence based on Machine Learning (statistical AI), data lakes have also been proposed [4,5]. Objectives: Monitoring farming and agronomical activities is based
-
Technologies de l'Information et de la Communication Field: Telecommunications / Machine Learning / Statistical Signal Processing. Research Lab: L2S (Laboratoire des Signaux et Systèmes) Advisor: Antoine BERTHET
-
with machine-learned quantum mechanical force fields trained on diverse chemical fragments. Sci. Adv.10, eadn 4397(2024) Where to apply Website https://ecolecentraledelyon.recruitee.com/ Requirements