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of Computer Science, you will: Lead research in your area of expertise, with a focus on machine learning and foundation models, and have the opportunity to establish and grow your own research group within a
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Eligibility criteria Selection will be based on the following scientific and technical criteria: • PhD in computational biology, machine learning, bioinformatics or a related field. • Proficiency with Python
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contact Enrico Glaab : Your profile We seek a bioinformatician or computational biologist who is well versed in the machine learning and statistical analysis of biomedical data, the use of artificial
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in privacy preserving machine learning (ML) within the SSF-ML-DH project, under the supervision of Olivier Cappé (CNRS, DI ENS) and Jamal Atif (Ecole Polytechnique, CMAP). Funding is available for two
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learning methods. Research Activities Conduct research on topics related to deep learning, reinforcement learning, the integration of machine learning and operations research, robust AI, trustworthy AI, and
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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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) and/or machine learning (about 10 PIs). The Physics Laboratory is about 180-member strong and conducts world-leading research on a broad range of topics, including quantum technology, statistical
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and economy that respect people and their environment. We are looking for our next postdoctoral researcher in computer graphics and machine learning to join the Image, Data and Signal (IDS) department
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Eligibility criteria The recruited person must have expertise in cosmology, numerical development and machine learning. They must be proficient in the Python programming language, with experience in JAX being a
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supplemented by seismological data—using machine learning methods (statistical or deep learning). As part of this thesis, we will use a dataset of approximately 1,000 numerically simulated earthquakes (already