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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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accessibility data will also be developed. The framework will be addapted and applied to spatial data to connect the GRN models to specific tissue phenotypes and to gain a better understanding of e.g. cancer
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processing, computer vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. The University may permit
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they are transmitted through populations. Research will have a strong focus on computational analysis or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale
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, volumetric analysis, and modeling of structural heterogeneity in biological macromolecules. Rather than only applying established workflows, you will explore new computational formulations and alternative ways
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project SCAPIS, medical images and clinical variables have been collected for more than 30,000 subjects. The goal is to use this dataset to train models for early detection of lung cancer (lung nodules
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accessibility data will also be developed. The framework will be addapted and applied to spatial data to connect the GRN models to specific tissue phenotypes and to gain a better understanding of e.g. cancer