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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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to microwaves, and will image the displacement of magnetic textures by currents, to quantify their efficiency in the generation of orbital currents and torques. Our approach will rely on the realization of new
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avec les partenaires, le suivi de la caractérisation des particules synthétisées, par différentes méthodes dont la diffraction des Rayons X, la spectroscopie IR, la microscopie électronique par exemple
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-DSC, MS) will be used to understand the mechanisms and selectivity by identifying reaction intermediates. The synthesized materials will be characterized by X-ray diffraction (XRD), FTIR, Raman
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for high-precision, dynamic biomechanical testing. This includes:* improving actuation control (speed and smoothness of motion),* ensuring synchronization with imaging systems (e.g., micro-CT, OCT
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biology OR virology A solid background in virology, transcriptomics and imaging Hands-on experience with experimental animal and/or iPSCs models Technical expertise in working in BSL-3 environment A
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), I³ (Inorganic–Isotopy–Imaging), ECOMES (emerging contaminants, speciation, and omics), Surface and Interface Characterisation, and Microbiological Characterisation. Together they enable an end‑to‑end
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). Under USM, policies developed by system administrators execute in user space, which greatly facilitates their deployment and maintenance. A functional prototype of USM already exists and has been used
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contribute to the development of a proof of concept obtained at University Côte d’Azur for accessing the content of a metabolomics knowledge graph (KG) with a large language model. It is Python prototype of a
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behavioral assays in the animal models Immunohistochemical analysis of neuronal, astroglial and microglial markers in zebrafish and mouse brain samples In vivo imaging of specific brain cells in the transgenic