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to fluorescently labelled AP-L complexes and analyze how the cells accumulate and spread the complexes over time using live cell imaging, immunocytochemistry, ELISA, Western blot, electron microscopy and other
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data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems
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of: Perform advanced microstructural and topological analysis of natural fibres using 3D imaging and image‑processing tools. Design and refine routes to isolate and tailor lignin fractions derived from
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-driven life science framework. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular
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Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace, mechanical
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imaging. Your Profile: * Master's degree in Computer Science, Electrical and Computer Engineering, Mathematics, Physics, or a related field * Strong background in mathematical and computational sciences
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), Computational Biology (stochastic and analytical models of gene expression), Signal Processing (machine learning, image and signal processing), Biophysics, Microbiology and Single-cell Biology (flow cytometry
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project. The project will also employ a PhD student at Lund University, focusing on developing hybrid architectures for deep learning-based image processing and methods for multimodal medical data. We will
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in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. ... (Video
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 29 days ago
in particular computer vision. Particular topics of interest include visual comprehension, hyperspectral imaging, numerical and parallel optimization, and unsupervised learning. A particular emphasis