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previously unknown gene functions. The work will be primarily computational, focusing on the development of deep neural network model architectures and their training. It will involve extending the preliminary
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of developed models - If necessary, extending current models concerning diffusion mechanisms - Verifying model robustness with respect to numerical parameters, mesh sensitivity, ... - Participating in academic
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well as the design of its future upgrade. The group is also actively working on LHCb's real-time analysis system and have a large interest in AI and neural-network based reconstruction methods. The LHCb
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Description Applications are invited for a postdoctoral position to study the neurocomputational mechanisms of information propagation in social networks. The Neuroeconomics group of Dr Jean-Claude Dreher
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temperature and water vapor profiles, or surface temperature. We search for the solution of an inverse problem, and we want to use a neural network model for this task. Our group has been using this type
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networks of micro-beams, can be optimized for their mechanical properties (elastic modulus, Poisson's ratio) [1,2]. However, they are prone to premature collapse due to strain localization bands, limiting
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will consist in creating phonetic categories from neural networks; more precisely, from SSL learning, we will seek to improve the models trained on an initial data set. we will collect these categories
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to advanced materials studies in support of Safran Ceramics' technical objectives. - Verify the model's numerical robustness through systematic evaluation of mesh discretization, parameter sensitivity, and
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in Quebec over these time scales. The project will be based on a network of regional study sites, including short (ca. 2000 years) and long (ca. 8000-9000 years, i.e., since deglaciation) sediment
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experimental set-up, and in particular will be responsible for setting up a local computer network and an elaborate control and acquisition chain, centered on a dedicated system already acquired by the team