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), the CLLE laboratory is recruiting a postdoctoral researcher in quantitative syntax and corpus linguistics Scientific background One of the aims of current work in quantitative and experimental syntax is to
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) project "Oc/Oïl : textes, identité et contact de langues aux confins gallo-romans" (Oc/Oïl: texts, identity and language contact on the Gallo-Romance borders), the Language Science researcher will play
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finite elements, finite volumes, etc., and knowledge of at least one programming language (preferably C++). English : level C2 Additional comments ERC Synergy NEMESIS Project (voir https://erc-nemesis.eu
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involving the human person, Some fluency in the French language will be necessary to conduct the tests on volunteers. Candidates should be experienced in the use of at least some of the techniques mentioned
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imaging modalities. Technical skills: Python, Java, C++, AI libraries (Tensorflow, PyTorch) Written and oral synthesis skills Language skills: English (read, written, spoken), French language desired
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post-processing and visualisation. Proficiency in at least one programming language. In addition, preference will be given to candidates who have: - experience in developing parallel numerical codes
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of selective adsorption of pollutants. In order to develop the skills needed to become an accomplished researcher, complementary training will be offered to the fellows: language courses (English, French) and
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graph theory, how language, memory, and executive functions interact depending on the neurodegenerative pathology. The recruited individual (M/F) is expected to present the study results to project
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of the statistical software R, and at least basic knowledge in one other programming language (Python, …) and database management system.• Experience with real, multivariate data analysis and biological data would be
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French National Institute for Agriculture, Food, and Environment (INRAE), Jouy-en-Josas | France | 4 days ago
based on the most recent deep learning approaches that train language models using few or no training examples by transfer learning or exploiting existing structured information, i.e. knowledge bases and