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experiments, as well as for its applications in medical imaging. For that, it collaborates with MedAustron, an ion cancer therapy and research center. Within this collaboration, we design and develop ultra-fast
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and image analysis (MATLAB or Python), machine learning techniques, and basic programming/coding will be a plus. Fluency in English is mandatory. Willingness to work in an inter-cultural and
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picture of spatial communication in typical and atypical ageing using cutting-edge methods from complementary disciplines. Fellows will benefit from training using techniques typically isolated to single
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our research team in the field of mixed-signal IC design, focusing on the implementation of sense to compute paradigm. The ideal candidate will have a strong background in circuit design and experience
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? No Offer Description We are seeking a highly motivated PhD candidate to join our research team in the field of mixed-signal IC design, focusing on the implementation of physics-based computing paradigm
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Research Institute for Environmental and Occupational Health (IRSET - UMR_S INSERM 1085) | Rennes, Bretagne | France | 18 days ago
such as molecular biology, flow or spectral cytometry, immuno-histochemistry, inflammatory signaling pathways, cell culture, imaging, and multi-omics analysis. Knowledge of environmental exposures and their
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) Project description: The project aims to select and create plants with increased albedo, which is the ability to reflect sunlight. Hyperspectral imaging methods and plant genetic modification will be used
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amphibians and other aquatic vertebrates. Using advanced imaging, neurophysiological, and molecular biological methods, we investigate the olfactory system. Furthermore, we use the olfactory network as a model
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. Development of advanced imaging and characterization technologies (X-ray micro tomography, EPR imaging and spectroscopy) to evaluate performance of electrodes and to map electrolyte chemical composition in
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual