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aims with regard to education and industrial development. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5505-GERBON-002/Candidater.aspx Requirements Research FieldComputer
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) To develop Deep Learning algorithms to significantly speed up probabilistic inference algorithms of current spatial birth-death models 2) To incorporate fossil stratigraphic and spatial information into a new
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goals include optimising convective heat transfer using wall oscillations, relating small-scale turbulence to heat transport, modelling large-scale outer flow effects, and developing low-order heat
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creation of a database for the various pollution sensors with a view to training online (non-embedded) models in the first instance. - Development of a machine learning algorithm based on the study database
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single needle, a robotic platform introduces complex motion control challenges that require the development of adaptive toolpath algorithm. This algorithm will ensure continuous and smooth deposition while
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modern technologies is a highly active topic at the interface of fundamental research and applications. The development of quantum technologies has been identified as a national strategic area. Furthermore
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of the Khovanov–Seidel representation in new cases, using Garside-type approaches. Developing extensions of Garside theory to the case of infinitely many atoms may also be of interest and will rely on the study of
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using wall oscillations, relating small-scale turbulence to heat transport, modelling large-scale outer flow effects, and developing low-order heat transfer models. Partnerships with industry will