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scientific studies and data recording for these collections, and is active both nationally and internationally in the field of cultural heritage conservation and analysis. The C2RMF is involved in
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-structure polymers, specifically designed for enzymatic degradation. The project will involve polymer synthesis, enzyme production and modeling, the advanced analysis of degradation products and the thorough
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for: +Preparing and characterizing ionic liquids (ILs) and natural deep eutectic solvents (NaDES) using FTIR, Raman spectroscopy, thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC
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- and nano-fabrication of devices (THz emitter and transistor) in a cleanroom, • optical and electrical measurements on the optimised devices, • analysis and interpretation of the data obtained
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thermodynamic analysis of chemical reactions. The PhD will be supervised by Sébastien Leveneur (HDR) and carried out within the framework of the ANR QUANTASTIC project, in close interaction with partners in
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investigate the impacts of SAI deployment through the analysis of modelling experiments, either based on GeoMIP simulations or by conducting various experiments on ad hoc scenarios developed by researchers in
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intended for a highly motivated and dynamic student with a strong background in synthetic organic chemistry. Knowledge and/or initial experience in (i) electrochemical and/or photophysical analysis or (ii
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RESEARCHER (M/F) with the ANR MIJMA (International Migration of Young Africans and Minors to Europe.
). The Laboratory of Economics and Sociology of Work (LEST, http://www.lest.cnrs.fr ) is a joint unit of the CNRS and Aix-Marseille University, specialising in the analysis of work and its various spheres as a
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analysis of biochemical and mechanical processes underlying phagosome maturation and resolution in living cells. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR8104-FLONIE-006/Default.aspx
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splicing. We will combine two areas of expertise: fine-grained analysis of DNA sequences (computer science) and understanding of cellular identity through embeddings trained on millions of cells