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(Machine Learned Potentials) type approaches, and/or multi-objective approaches. - in-depth knowledge of Python programming languages (or C++, Fortran) and the Unix system; - Certified level in written and
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(which is often easier to create particularly in for multilingual processing typically by using machine translation) and further improving the model using preference data. Preference learning has gained
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will focus on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural
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oscillation analyses • Practical Experience of deploying state-of-the-art machine learning techniques Desirable: • Ability to develop and apply new concepts • Verbal and written communication skills • Ability
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the suited security micro-services. This automation is made possible by formalizing micro-services-based applications and their data-flows, and machine learning techniques for selecting the micro-services
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processing, involving machine learning techniques, as well as active participation in data collection from the detectors deployed on site. - Analysis of particle physics data applied to muography: filtering
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], e-learning robotics based on human gestures [6] or active exoskeletons [7]. The aim of this thesis is therefore to understand human-human collaboration strategies in order to develop a coherent
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selection, the marginal likelihood, and generalization. In International Conference on Machine Learning, pages 14223–14247. PMLR, 2022. [8] B. O. Muth ́en. Beyond SEM: General latent variable modeling
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particular to optimise the design of the instrument for maximum spatial resolution; ii) evaluate the contribution of machine learning and deep learning methods to jointly estimate the 3D distribution of GP
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and machine learning Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8254-SYLDES-005/Default.aspx Work Location(s) Number of offers available1Company/InstituteLaboratoire