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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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Unravel the complexity of valve disease in heart failure using Digital Twin technology. Help transform how cardiologists decide when and how to treat patients through personalized computer
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well as with members of the Biomedical Mass Spectrometry and Systems Biology Section at the Department of Biochemistry and Molecular Biology. Lab and Research Environment You will be part of the research group
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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Predoctoral researcher at the Targeted Therapeutics & Nanodevices Research Group (Project BRAINZYME)
of scientific results for designated conferences, specialized journals and possible patent application processes. • Help others with general lab-keeping tasks, such as inventory logs, ordering, instrument
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transmigration and infiltration into the tumor microenvironment Requirements for candidates: Essential: BSc and MSc in biotechnology, biomedical engineering, biomedicine or similar areas Candidates should be ready
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, biomedical engineering, biomedicine or similar areas Candidates should be ready to enter an official doctoral programme in December 2024 (under Spanish Law). By this time, they must have obtained a university
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about biomedical research or cell biology? Ready to make groundbreaking contributions to understanding ciliopathies through innovative machine learning approaches? Join the Cilia-AI consortium, and embark
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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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- and time-specific innervation that extends into adolescence. Our lab has used whole-brain tissue clearing, light-sheet imaging, and machine learning to map the spatial and temporal dynamics of serotonin