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statistical inference. More specifically the recruited PhD student will consider variational inference approaches for GEA and stochastic optimization to speed up the inference, with the objective of scaling up
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, technicians, and staff. Each year, many undergraduate and graduate students, PhD candidates, postdocs, and visiting scientists join its activities. Research at LPS covers a broad range of condensed matter
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-permanent staff (doctoral students, postdocs, associate researchers and interns). The ICS has characterization platforms (UV-Vis and IR spectroscopies, size exclusion chromatography, light scattering, etc
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, including 180 permanent staff (researchers, professors, engineers, technicians, and administrative personnel) and around 180 non-permanent staff (PhD students, postdocs, and fixed-term contracts). Each year
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 6 days ago
arithmetic cores for FPGAs). The team hosts 6 faculty, 6 PhD students, 3 postdocs, 2 engineer, and multiple research interns. Additional information can be found on team website: https://team.inria.fr/emeraude
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be done via computer simulations, including Monte Carlo and molecular dynamics, combined with the use of statistical mechanics to predict e.g. phase transitions, nucleation rates, etc. The work will be
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(Probability, Statistics and Modeling Laboratory, CNRS-Université de Lorraine), EDF (Electricité de France), and Fives-Prosim. This doctoral program focuses on generative models for energy cycles. Its main
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conditions. Profile: Required degree: Master's (M2) in Ecology and Evolution - Specialization: Modeling in ecology and evolution, theoretical ecology Expected skills: - Statistical analysis - Mathematical and
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particular NLP, statistical learning, machine learning, generative AI, and their major fields of application. Roles and responsibilities The applicant will join the team of the 3IA Côte d’Azur Institute and
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industrial process control remains under-explored; the current approach relies on statistical tests or conventional machine learning. One of the manufacturing processes addressed in this thesis is injection