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into their molecular components in time and space, from single molecules to native tissue environments. The position is based in the Associative Learning Group at Department of Experimental Medical Science, Medical
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, with a particular focus on identifying and characterizing rare endosomal escape events. The tasks include developing, training, and validating deep learning–based models for event detection and vesicle
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vision), and the division of statistics and machine learning at the department of computer and information science (focusing on the theory behind machine learning). The employment When taking up the post
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Project description Third-cycle subject: Biotechnology The project aims to develop probabilistic deep learning models
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recruiting an outstanding and ambitious postdoctoral researcher in computational biology to advance the integration and modeling of large-scale microscopy data using modern machine learning approaches
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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years according to central collective agreement. Full time position. Starting date as agreed. Placement: Uppsala For further information about the position, please contact: Ruisheng Xiong (e-mail
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, serving the SciLifeLab infrastructure and the Data Driven Life Science (DDLS) research program. The Data Centre has a national assignment and operates a range of life science data and e-infrastructure
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is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here. Are you interested in learning more? Read about Umeå university
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, spatially aware QC, normalization, cell segmentation integrating H&E imaging and transcript density, and spatial domain identification Single-nucleus integration and annotation: large-scale multi-sample batch