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lead sophisticated multimodal bioinformatics integration and analysis, collaborating daily with a diverse team of computational scientists and clinicians to translate complex data into actionable cancer
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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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degree. Applicants with experience in one or more of the following fields are encouraged to apply: Organoid culture Flow cytometry Microscopy Bioinformatics analysis Previous publications are considered a
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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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, 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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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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Biology is organized into seven research programmes which all focus on different areas of cell and molecular biology: Computational Biology and Bioinformatics, Microbiology and Immunology, Molecular Biology
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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