119 machine-learning "https:" "https:" "https:" "https:" "https:" positions in France
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- l'institut du thorax, INSERM, CNRS, Nantes Université
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(including computer science, machine learning or deep learning). Activities Description of the research activities : The post-doctoral researcher will develop the research actions defined in his/her research
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22 Oct 2025 Job Information Organisation/Company Universite de Montpellier Department Human Resources Research Field Biological sciences Technology » Computer technology Researcher Profile
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knowledge of multi-objective problems. Master students or Engineers in the field of Process Systems Engineering are strongly encouraged to apply. Knowledge of machine learning algorithms, energy markets and
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this goal, it is paramount to characterize the added value of using machine learning in estimating and decoding quantum errors occurring in coded quantum systems. Research program: The PhD student will first
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training, as well as on machine learning or generative AI models. Technology watch on AI recommendation models and optimization of recommendation algorithms. Implementation of the recommendation engine
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being responsible for the confocal microscope (maintenance). Resources provided (equipment, IT, etc.): office equipped with a computer workstation, equipped laboratory bench, reagents and equipment shared
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 3 months ago
the Statify team, located in Inria Montbonnot. He or she will have access to a team of experts in high-performance computing and machine learning that will help him or her to kickstart the project under
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within the ANITI HUCAD project which aims to develop Artificial Intelligence (AI) systems fostering human-machine collaboration for effective deliberations. It aims to enhance the argumentation
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) - Implement a demonstrator of the platform where a human learns how to operate a CNC machine with the help of a social robot, through the digital twins and interfaces. This includes: o Defining a learning
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foundations in classical probability theory and can be seen as a generalization of the Bayesian framework, bringing an additional degree of flexibility to express different types of uncertainty. In machine