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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
-tier machine learning and computer vision venues, actively participating in departmental seminars, and contributing to collaborative projects. Where to apply Website https://jobs.inria.fr/public/classic
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. in Computer Science or a closely related field, with research focus in Reinforcement Learning, Machine Learning, Embodied AI, or AI for Education. Strong background in deep reinforcement learning, with
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learning (ML). Our lab explores the intersection of artificial intelligence, and human-computer interaction, striving to create technologies that amplify human potential. The successful candidate will engage
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at the atomic scale, using density functional theory-accurate machine-learned potentials and molecular dynamics simulations, in close collaboration with leading European research institutes and steel industry
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scattering and direct dark matter detection, using advanced cryogenic detectors. The MBI hosts a research group dedicated to Machine Learning in Particle Physics. Qualifications: PhD in Particle Physics
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Qualifications Experience with machine learning and deep learning Ability to analyze and interpret complex geophysical data and apply appropriate research methodologies Additional Information: The College of Arts
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are especially encouraged: (1) statistical mechanics, condensed-matter theory, quantum field theory; (2) tensor networks, quantum information, quantum algorithms; (3) machine learning, generative models, large
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, bioinformatics, or other related disciplines is required. Strong interest, research background and experience in the methodology research in statistical genomics, machine/deep learning, bioinformatics methods in
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position within a Research Infrastructure? No Offer Description The Machine Learning for Health team at the Institute for Molecular Medicine Finland (FIMM) , University of Helsinki, is currently seeking a
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in the area of probabilistic methods in machine learning. These include applications of Random Matrix theory and Gaussian processes to Deep Neural Nets. Teaching may be required. Education and