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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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. Proficiency in standard molecular and cell biology techniques, including RT-qPCR, ELISA, immunocytochemistry, and Western blotting. Experience with sample preparation, imaging, and image analysis using confocal
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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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molecular and cell biology techniques, including RT-qPCR, ELISA, immunocytochemistry, and Western blotting. Experience with sample preparation, imaging, and image analysis using confocal and high-resolution
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for general criteria for the position. Preferred selection criteria Familiar with use of real-world data Experience with fieldwork Experience with signal and image processing Personal characteristics
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position We have a vacancy for a PhD candidate in machine learning
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. 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 unable to load
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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 unable to load from YouTube. Accept
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. 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 unable to load
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. This highly innovative project aims to develop a fully AI driven digital twin that enables real-time optimization and control of fermentation processes. The candidate will develop the digital twin for microbial