486 structures "https:" "https:" "https:" "https:" "https:" "https:" "University of Southampton" positions at CNRS
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at CRHEA (Valbonne, 06). It will focus on the epitaxy of these new structures using MBE and MOVPE techniques and their structural and electrical characterizations which will be validated by the partners
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into key intermediates such as FDCA and DFF. And to a lesser extent to: • The development of hybrid photo–bio catalytic systems • The study of structure–activity relationships and system integration
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. The project is carried out in a collaborative research environment. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR9025-DANCOM-005/Candidater.aspx Requirements Research FieldPhysicsEducation
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identification of microbial species that coevolve with the host immune system. These findings will support models of immune dynamics that can predict age related immune responses. Where to apply Website https
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chemistry synthesis (organic and coordination chemistry) - intercalation and exfoliation chemistry - synthesis in supercritical media - structural and spectroscopic characterisation - magnetic and
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platforms (nano-XRF imaging, aging test benches, X-ray microtomography, etc.). Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5129-MARCLO-108/Candidater.aspx Requirements Research
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developmental) and integrative neuroscience, the laboratory is structured around four main teams: - Cognition-Behavior-Context - Social Behaviors and Collective Dynamics - Physiological and Psychosocial Stress
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molecules using site-specific conjugation strategies. - Development of DNA origami scaffolds to spatially organize and orient proteins for structural studies by single-particle Cryo-Electron Microscopy
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and associated with biological processes such as the circadian rhythm. The aim of the project is to understand at a molecular level the impact of these modifications on the structure and dynamics
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surface-chemistry trends across selected metals and their oxides. These data will support the construction of a machine-learning force field tailored to NHC–surface systems, enabling large-scale molecular