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. The computational work includes, for example, the analysis of omics data and computational modeling. Experience in cell culture, molecular cloning, and bioinformatics analyses is required. Proficiency in statistics
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physiological balance. Perform LC/MS based proteomic analyses of circulating proteins and assess their impact on organs in mouse models and cell cultures. Analyze and interpret omics data using bioinformatic
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the equivalent of a doctorate in statistics, bioinformatics, mathematical statistics or equal subject is eligible for appointment as postdoctoral researcher. This eligibility requirement must be met no later than
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responsibilities include bioinformatics analyses with a strong focus on single-cell RNA sequencing and next-generation single-cell barcode lineage tracing, as well as the interpretation of complex biological
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, bioinformatics, or biostatistics. Practical experience within the respiratory field, with a combination of wet-lab and biostatistics/bioinformatics experience is desirable. Specific experience with lung samples
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develop and improve protein-glycan binding prediction models and use AI, data science, and bioinformatics to identify and design glycan-binding proteins with desired binding specificities. Qualifications
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on different projects related to biotechnological methods for producing recombinant silk proteins, characterization of these, spinning of fibers, protein engineering, material characterization and bioinformatics
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in mouse models and cell cultures. Analyze and interpret omics data using bioinformatic pipelines in Python and R. Perform experiments in cell culture and animal models to validate the findings
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cytometry and RT-qPCR. The computational work includes, for example, the analysis of omics data and computational modeling. Experience in cell culture, molecular cloning, and bioinformatics analyses is
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Experience with serology and high-parameter flow cytometry Expertise in statistical and bioinformatic analysis of immunological datasets Experience with the development or use of viral infection models