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cell lines, primary and iPSC-derived cells, and animal models Conducting microscopy experiments using conventional and super-resolution imaging techniques Conducting experiments using chemical biology
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. Activities – Maintenance and monitoring of IT and experimental systems – Generation of analysis pipelines for electrophysiological recordings – Generation of image analysis pipelines for 3D reconstruction
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-supervision by a doctor and a statistical/machine-learning researcher is planned (iBV / Inria) 1- Context and Objective: Monitoring tumor response using clinical imaging, such as CT or FDG-PET, has become a
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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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. This postdoctoral position is part of the EU cofund research project AMBER, Advanced Multiscale Biological imaging using European Research infrastructures, will address scientific and sectoral gaps in biological
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techniques have been developed to overcome this drawback. Among them we focus on fluctuation of fluorescent molecules methods as they don’t need any specific materiel or fluorophore. The super-resolved image
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reconstructing the super-resolved image is formalized as an inverse problem which is regularized by introducing suitable sparsity constraint. In our team, we have recently proposed both model-based and data-driven
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diverse non-model organisms, such as Asgard archaea and microbial eukaryotes, using a combination of cryoET, cryoEM and complementary imaging techniques. For examples of our work, see https
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, but are not limited to, neural coding in the visual cortex, multimodal information processing, state-dependent processing, visual perception, development of imaging tools for in vivo neuronal recordings
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algorithms will be developed to extract discriminative and predictive features from a multimodal dataset consisting of digital histopathological images, lung CT images, clinical, genomics, and multiproteomics