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between machine learning and robotics. The first part of the project will involve working with members of various CNRS laboratories to map the main open-source tools made available to the community by
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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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of Research Experience1 - 4 Additional Information Eligibility criteria - PhD in Phonetics/Phonology, Computational Linguistics, Automatic Speech Processing/Machine Learning or relevant related fields
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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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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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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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(segmentation analysis by machine learning) and automatic language processing on large quantities of digitised historical photographs and their metadata. - management, enrichment and structuring of project data
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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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(FL) is a promising paradigm that is gaining grip in the context of privacy-preserving machine learning for edge computing systems [1]. Thanks to FL, several data owners called clients (e.g