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regulatory networks involved in the STING pathway in the model organism Drosophila melanogaster. Establish analysis pipelines to identify candidate STING regulated genes based on regulatory sequences. Related
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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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collaborative network. The selected candidate will join a multinational team entitled "Antiviral innate immunity in Insects : Evolutionarily conserved mechanisms and innovations" within the laboratory “Insect
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research & research methods Strong analytical skills and a collaborative mindset Experience with (Bayesian) statistics, deep neural network models, Kernel Methods, or data science Experience with neural data