123 machine-learning-"https:" "https:" "https:" "https:" "https:" positions in France
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- CNRS
- Inria, the French national research institute for the digital sciences
- Institut Pasteur
- Grenoble INP - Institute of Engineering
- IFP Energies nouvelles (IFPEN)
- IMT Atlantique
- IMT Mines Ales
- Nature Careers
- Universite de Montpellier
- École nationale des ponts et chaussées
- Arts et Métiers Institute of Technology (ENSAM)
- Ecole Normale Supérieure de Lyon
- Université de Bordeaux / University of Bordeaux
- Aix-Marseille Université / CNRS
- Bioptimus
- CEA-Saclay
- Centre de recherche en Automatique de Nancy
- ESRF - European Synchrotron Radiation Facility
- Ecole Normale Supérieure
- European Synchrotron Radiation Facility
- Fondation Nationale des Sciences Politiques
- Grenoble INP - LCIS
- IMT Mines Albi
- INSA Strasbourg
- Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP)
- Institute of Image-Guided Surgery of Strasbourg
- Nantes Université
- Télécom Paris
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE
- Universite Grenoble Alpes
- University Paul Sabatier
- University of Avignon
- University of Rouen Normandy
- University of Strasbourg
- Université Bourgogne Europe
- Université Grenoble Alpes
- Université Savoie Mont Blanc
- Université Sorbonne Paris Nord
- Université de Caen Normandie
- Université de Strasbourg
- l'institut du thorax, INSERM, CNRS, Nantes Université
- université Strasbourg
- École polytechnique
- 33 more »
- « less
-
Field
-
team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at the interface of machine learning and biology (tools developed
-
of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
-
details of 2-3 references to laura.cantini@pasteur.fr For more information : https://research.pasteur.fr/en/team/machine-learning-for-integrative-genomics/
-
Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 26 days ago
, curious, autonomous, proactive and dynamic. A specialization in optimization, machine learning, statistical learning or game theory is appreciated. Research experience is a plus. LanguagesFRENCHLevelBasic
-
, proteomics, metabolomics), Capacity to develop and/or apply : Statistical or mathematical models Machine learning / AI methods Systems biology modeling approaches Research position The fellow will conduct
-
Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 9 days ago
modules leveraging deep learning for classical problems such as segmentation and 3D object tracking interfacing machine learning code and the robot using ROS2 contributing to the creation of datasets
-
or Phonetics Basic knowledge of machine learning tools; familiarity with a scripting language Ability to communicate and coordinate with different partners: field linguists, computer scientists, engineers
-
Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 10 days ago
machine learning, 3D visualisation and real-time images (augmented reality). The main robot will the Tiago++, by PAL Robotics, which is a modern omnidirectlonal, bimanual robot. With the help of a PhD
-
Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 5 days ago
-PULSE addresses a key open question in responsible AI: can we design practical machine learning systems that satisfy strong privacy guarantees [1] and fairness [2] constraints simultaneously, without
-
École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 16 hours ago
computational mechanics and scientific machine learning. The successful candidate will work on the design of hybrid, physics-informed modeling and identification frameworks for complex dissipative material