Sort by
Refine Your Search
-
Category
-
Employer
-
Field
-
organizational skills in English (working language). For any further information please visit Prof. Dr. Dirk Brenner: https://orcid.org/0000-0001-8979-1045 Applications including a cover letter and a curriculum
-
Recombination team is a multinational team (France, Switzerland, Italy, India) composed of three researchers, two engineers, one PhD student, and several master students. The working language is English. The
-
of proof assistant technology capable of understanding the dynamical linguistic structures found in current high-level mathematical texts. The project includes the study of interpretation mechanisms
-
, attached mainly to Section 34 (Language Sciences) and secondarily to Section 26 (Cognition, Brain, Behavior). It is a multidisciplinary research unit in Cognitive Sciences. The PhD candidate will join the
-
presentation techniques - English language: B2 - Russian language: B2 - Ukrainian language: C1 - Ability to develop data collection tools (questionnaires, interviews, case studies, field observations, monographs
-
French language will be required by the Montaigne-Humanités PhD School before the end of the first year of the thesis. Applicants who have previously completed a PhD degree cannot apply for this position
-
., topic modeling, transformer models). Experience with historical or cultural datasets. Competence in a relevant non-Western language (e.g., Farsi, Hindi, Japanese) or willingness to acquire basic
-
with both the CNRS and UGA, organized into five research teams conducting work in cognitive science (Body and Space, Development and Learning, Language, Consciousness, Memory & Metacognition, Vision
-
Requirement:: -Master in organic chemistry -Lab experience -Language: French and English Website for additional job details https://emploi.cnrs.fr/Offres/Doctorant/UPR2301-LUCNEU-012/Default.aspx Work Location
-
that are transforming many sectors today through language models, recommendation systems and advanced technologies. However, modern machine learning models, such as neural networks and ensemble models, remain largely