72 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions in France
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Nature Careers
- CNRS
- Institut Pasteur
- École nationale des ponts et chaussées
- CEA
- Inria, the French national research institute for the digital sciences
- Télécom Paris
- Ecole Normale Supérieure
- Ecole Normale Supérieure de Lyon
- French National Research Institute for Agriculture, Food, and the Environment (INRAE)
- IMT Atlantique
- INSA Rouen Normandie
- Institut polytechnique UniLaSalle
- LAUM UMR CNRS 6613
- Sciences Po;
- University of Rouen Normandy
- University of Strasbourg
- Université Angers
- Université côte d'azur
- Université de Caen Normandie
- Université de Pau et des Pays de l'Adour
- 11 more »
- « less
-
Field
-
double degree and requiring international mobility. This offer is part of the MSCA-COFUND CHORAL programme, which will finance 14 positions out of 20 positions offered. Where to apply Website https
-
, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
-
Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 2 months ago
the machine learning community as challenging, high-dimensional testbeds. Notably, the recently developed WOFOSTGym simulator \cite{solow2025wofostgym}, bridging crop modeling and RL, received the Outstanding
-
, Éducation en Océanie” [https://heliceo.huma-num.fr/ ] est un consortium scientifique du CNRS, prévu sur plusieurs années (2025–2030), et pour l’instant financé pour les 12 premiers mois. Il vise la
-
. The successful candidate will be employed at the Department of Computer Science of the University of Luxembourg and have access to high-performance computing resources suitable for large-scale machine-learning and
-
for experimentation, yet they remain difficult to deploy directly onboard robots due to hardware availability, latency, sampling cost, and noise. Previous work on quantum machine learning (QML) emphasize
-
significant computational component. We strongly recommend a background in machine learning and coding. Applicants with a background in areas such as computational neuroscience, reinforcement learning, or deep
-
should have a graduate degree (Master 2 degree). Him/her scholar background should include: • statistical/machine learning, statistical inference, clustering, classification • deep learning, variational
-
within the project AI4TECSWriting a doctoral dissertation in computer sciencePublishing research findings in leading international conferences and high‑impact journals in AI, machine learning, and
-
strong background in optimization and machine learning. Good coding skills in Python, PyTorch are welcomed. Application Applications should contain a CV, a motivation letter, the grade records of the last