333 structures-"https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" research jobs at CNRS in France
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charge/discharge cycles. The aim is to provide a unique tool to better understand the structure and evolution of interfaces/interphases in batteries, and thus, guide the design of more efficient and
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diffractometer, a GC for common gas analysis, a GC/MS, and an HPLC/MS. DFT calculations will be performed using annual allocations on national high-performance computing centers. More details here: https
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the development of flexible nanozeolites. This position offers a unique opportunity to explore the structural dynamics of nanozeolites under varying conditions and contribute to advancing green
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neuroimaging data constrained by patient's structural connectivity and tractography • Using the results of the TVB model fits to stratify patients and predict disease progression • Organizing and unifying
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Website https://emploi.cnrs.fr/Candidat/Offre/UMR7107-ANIFOR-019/Candidater.aspx Requirements Research FieldLanguage sciencesEducation LevelPhD or equivalent Research FieldLanguage sciencesEducation
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of the ANR Lamorsim project (https://anr.fr/Project-ANR-23-CE08-0029 ). The main objective of this project is to develop methodologies for achieving controlled laser-induced amorphization of silicon. In this
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of contributions to the international ePIC (electron Proton-Ion Collider experiment) collaboration associated with the construction of the future Electron-Ion Collider (EIC, Brookhaven National Laboratory -BNL, New
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(exceptional points, bound states in the continuum, etc.). He/She will in charge of the design of the corresponding photonic structures. To this end, he/she will conduct design/simulation campaigns using not
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model's development. The position is based at LOCEAN on the Pierre and Marie Curie campus of Sorbonne University. The LOCEAN laboratory (https://locean-ipsl.upmc.fr ) is one of nine laboratories in
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the flexibility and power of NNs with the ability of LMMs to robustly learn from structured and noisy (non i.i.d.) data, applying them on the prediction of both plants and human phenotypes. These models will