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, longitudinal patient and population registries and biobanks. Project description The development of artificial intelligence (AI) and computerised image processing in combination with advanced digital microscopy
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model and biological membranes. The experimental data will be paired with results from molecular dynamics simulations to provide a complete characterization of the biophysical properties of the imaged
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proteomics approaches. The position focuses on practical, microscopy-based work in human and animal tissues, supporting various projects aimed at understanding biological processes in health and disease
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KTH Royal Institute of Technology, Scool of Electrical Engineering and Computer Science Job description Cellular morphology reflects fundamental biological processes such as division
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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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collaboratively Meritorious qualifications: Experience with machine learning or deep learning Experience with computer vision or image analysis Experience with 3D data, geometric representations, or parametric
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. Experience in deep learning, computer vision, or neural network development. Experience with live-cell microscopy, fluorescence microscopy, or analysis of 3D/4D image data. Experience in cell biological
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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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super‑resolution microscopy and quantitative live-cell imaging using diverse fluorescent reporters in cultured cells and early embryos. These experimental approaches are integrated with advanced image
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Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes