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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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. 5, no. 2, pp. 354–379, 2012. [2] C. K. Williams and C. E. Rasmussen, Gaussian processes for machine learning. MIT press Cambridge, MA, 2006, vol. 2, no. 3. [3] G. Daras, H. Chung, C.-H. Lai, Y
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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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solid experience in programming, particularly in Python and JavaScript. Significant experience in data science and machine learning will be highly valued. You like to work in a team while demonstrating
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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
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). • Advanced quantitative analyses (machine learning, computer vision, multilevel statistics). • Creation and use of Python code for advanced analyses. • Management and monitoring of complex transgenic lines
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FieldPhysicsYears of Research ExperienceNone Additional Information Eligibility criteria We are looking for a colleague with a PhD in particle physics. Experience with machine learning and/or experience with
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l'institut du thorax, INSERM, CNRS, Nantes Université | Nantes, Pays de la Loire | France | about 1 month ago
LevelPhD or equivalent Research FieldMathematicsEducation LevelPhD or equivalent Skills/Qualifications Must hold a Ph.D. degree in Mathematics / Computer science or Machine Learning. • Be able to work within
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 2 months ago
to apply Website https://jobs.inria.fr/public/classic/en/offres/2025-09273 Requirements Skills/Qualifications We are seeking highly motivated candidates with a background in machine learning and medical
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cause abnormal or unsafe behavior. (2) Evaluate their effects on performance, safety, and security metrics. (3) Propose and validate mitigation and hardening techniques at the model, system, and learning