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myeloma, as well as to understanding immune aging processes, through comprehensive functional, phenotypic, and genetic analyses of large, dedicated patient biobanks. To elevate our research to the next
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analytical chemistry and mass spectrometry. The project involves developing characterization methods using mass spectrometry (FT-ICR, TOF-SIMS) and imaging techniques (SEM, TEM) for both biological and
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data processing, image/signal analysis, and machine learning. ✅ Familiarity with instrument control, calibration, and automation workflows. ✅ Excellent written and oral communication skills in English
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volunteers and one in patient populations. Be involved in all aspects of the research: recruitment, data collection, management, public and patient involvement, imaging processing, and analysis. Liaise with
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microtubule nucleation is regulated in the context of neuronal development within Drosophila. The recruited postdoctoral fellow will use powerful genetics and advanced cell imaging of neurons in situ
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biomedical image processing. Within the scope of machine learning and computer vision, there will be freedom to suggest your own research directions, and to become acquainted with new techniques and approaches
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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advanced instrumentation (cellular and animal models, proteomics, gene editing, lentiviral transduction, live cell imaging, super-resolution microscopy, etc.). For more information on the research themes in
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the Quantitative Imaging Platform of Villefranche (PIQv; https://sites.google.com/view/piqv ), which oversees the operation of the tools that the team develops. Those tools include imaging sensors, such as the
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co-culture systems Have prior experience in molecular biology techniques, such as RNA sequencing, Western blotting, qPCR, immunocytochemistry, or live-cell imaging Show an aptitude for designing and