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systems, flow cytometry, and exosome research proficiency in R programming for statistical and bioinformatics applications, and practical knowledge of Linux environments experience in scientific writing
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RNA-seq data from single cells. For this, you need to be proficient in using existing tools for bioinformatics analysis. The work is varied, and there are great opportunities for personal development
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bioinformatic pipelines. The analyses will be carried out on GPUs and part will consist of data processing and visualization in order to facilitate interpretation and, in some instances, clinical reporting
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involves collecting clinical data on the effects of childhood cancer treatment, bioinformatically handling sequence data and developing prediction models, as well as conducting Single Cell RNASeq studies and
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environments. • Self-motivation and curiosity, with a strong drive to explore new research directions independently. The following education, experience and expertise are required: A PhD degree in bioinformatics