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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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disciplines and involve expertise in optics, electronics, image and data processing using machine learning, photophysics, chemistry and biology. The position is therefore particularly well suited for candidates
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 17 days ago
. [9]). We are particularly interested in improving the selection of transmission opportunities (e.g., using precomputed sequences), possibly constructed with machine learning techniques (as in [8]). We
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | about 2 months ago
imaging and with interest in translating research to clinical context. We require expertise in Machine Learning and Image Processing, notably Image Segmentation. Knowledge in Medical Imaging is desirable
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, which may include machine learning. This could involve medical image analyses and processing or biomarker quantification approaches. Advanced expertise: Experience in Biophotonics, which may include light
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a team More specifically: - For mission 1: knowledge of signal and image processing, machine learning (PyTorch or TensorFlow + NumPy/SciPy), statistical processing & data and results visualisation
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experience in computational methods, which may include machine learning. This could involve medical image analyses and processing or biomarker quantification approaches. Experimental set up experience would be
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of massive galaxies from the primordial Universe to z~2. This project combines a unique JWST dataset with state-of-the art hydrodynamical simulations and machine learning techniques to understand the origins
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). • Advanced quantitative analyses (machine learning, computer vision, multilevel statistics). • Creation and use of Python code for advanced analyses. • Management and monitoring of complex transgenic lines
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, statistics, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology