Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
3 Mar 2026 Job Information Organisation/Company CNRS Department Sciences et Ingénierie, Matériaux, Procédés Research Field Chemistry Physics Technology Researcher Profile First Stage Researcher (R1
-
visit the website: https://www.is2m.uha.fr/ . The thesis will be attached to the doctoral school of physics and physical chemistry (ED182), which is co-accredited between Unistra and UHA. The doctoral
-
23 Feb 2026 Job Information Organisation/Company CNRS Department Laboratoire d'analyse et d'architecture des systèmes Research Field Engineering Physics Technology Researcher Profile First Stage
-
ultrasound, Laboratoire d'imagerie Biomedical, LIB , https://www.lib.upmc.fr/ ) and nanoparticle engineering ( PHENIX Laboratory https://phenix.cnrs.fr/ ). The LIB is located in the Centre de Recherche des
-
together expertise in physics, chemistry, nanoscience, and materials engineering. For more information about IS2M, please feel free to visit the website: https://www.is2m.uha.fr/ . The PhD candidate (M/F
-
11 Mar 2026 Job Information Organisation/Company CNRS Department Laboratoire de chimie des polymères organiques Research Field Chemistry Physics » Chemical physics Physics » Biophysics Researcher
-
specialist of the sociology of Wikipedia and a senior data engineer from the Wikimedia Foundation. Candidates (M/F) should hold a Master's degree in Theoretical Physics and/or Complex systems, and have a good
-
Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
-
Research Framework Programme? Horizon Europe - ERC Is the Job related to staff position within a Research Infrastructure? No Offer Description The Machine Learning for Integrative Genomics team (https
-
AI researchers from ANITI, IMT and CERFACS, as well as with researchers/engineers in weather forecastings from the CNRM (Météo-France). Hybridization methods between neural networks and physical models