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, as well as with industry and society. The faculty entertains various successful and growing teaching programs and supports lifelong learning activities. Your role As a professor in the Department
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LPSM (Laboratoire de probabilité et modèles aléatoires) in Paris. Main mission : The project lies at the interface between quantitative ecology and statistical learning. It brings together the expertise
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Context Recent advances in computer vision and generative AI have enabled major breakthroughs in image and video understanding. However, modern deep learning models remain critically dependent
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platforms and 1,600 partner companies. For more information: https://www.imt-mines-ales.fr You will be joining CERIS, which is home to two research units: the SyCoIA (Complex Systems and Artificial
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
machine learning, explainable machine learning, fairness and data protection legislation. Privacy-preserving machine learning aims at learning (and publishing or applying) a model from data while the data
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learning. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR7154-SABGAL-060/Default.aspx Requirements Research FieldGeosciencesEducation LevelMaster Degree or equivalent Research
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biomedical applications, with a strong emphasis on neurodegenerative diseases. Your role Act as the core developer for software projects within the group, translating cutting-edge deep learning algorithms
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to strengthen your skills, acquire new ones, and enhance your mission. A work-life balance recognized by our team members. Remote work options to reduce commuting time and improve your quality of life. A rich
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medicine, nursing sciences, and midwifery. The role includes the implementation of a state-of-the-art public health curriculum, incorporating advanced educational tools such as digital learning platforms
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educational tools such as digital microbiology platforms, virtual labs, simulation-based training, and case-based learning. Working closely with faculty members and clinical partners, the successful candidate