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according to experience. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/IRL3218-GUICAR-009/Candidater.aspx Requirements Research FieldEngineeringEducation LevelPhD or equivalent Research
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 2 months ago
collaborations, depending on the evolution of the work and the experimental setup. There is no industrial partnership associated with this project. Assignments : With the help of the scientific supervisors
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lining of blood vessels— which plays a central role in the development of vascular diseases. Blood vessels further complicate this picture through their complex geometries, featuring diameter variations
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) the creation of new-based DNA nanostructures as a molecular template to hold proteins to facilitate molecular imaging by single-particle Cryo-Electron Microscopy (Cryo-EM) (2) the development of DNA-based nano
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purifying proteins from bacterial expression systems. -Participating in quantitative interactomics experiments and data analysis. -Development of cellular assays to validate interactions Required
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to the development of an innovative microphysiological system dedicated to modeling human beige adipose tissue organoids in a controlled metabolic environment. The candidate will design and implement advanced 3D
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with the scientific scope of the beamline and the ESRF research strategy. The postdoctoral fellow will join the team responsible for the operation and continuous development of the ESRF beamline ID12, a
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other Deputy Directors and with the support of the Finance Directorate; Participating in the development, implementation, and monitoring of the multiannual strategy and the annual objectives roadmap
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rely on the development of models integrating different data sources, initially based on data simulations, consistent with the data sets that are actually available or planned for acquisition, as
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from limited data; development of automated and interactive pipelines. • AI and image processing for anomaly detection: data acquisition (dataset creation and validation), segmentation of realistic data