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pathology. Your research will focus on answering critical questions related to both healthy and diseased liver, utilizing data from improved wet-lab protocols from multiple biological models, complemented by
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information may be found in the following selected references from the lab: Xenografted human microglia display diverse transcriptomic states in response to Alzheimer's disease-related amyloid-β pathology. Nat
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, bioinformatics, structural biology, analytical chemistry and chemical synthesis. For more information on our lab and publications, please visit https://masscheleinlab.sites.vib.be . Our lab hosts state-of-the-art
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bioinformatics) via regular online meetings and short research stays. In this way, your data will contribute to building a model of the regulatory code. You will perform experimental validation by using this model
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on an administrative level, allowing you to focus on the science, both hands-on in the lab and analyzing data. Responsibilities Utilize computational tools and techniques to model and analyze the structure and function
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of transcriptomics and genomics datasets. Desirable Requirements Experience in single-cell and spatial OMICS data analysis. Development of ShinyApps and experience with large-scale dataset management. Knowledge
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Postdoctoral position available to study human-specific molecular mechanisms of neuronal development
to apply? Please complete the online application procedure and include a detailed CV incl. list of publications, a motivation letter, and the contact information of three referees. For more information
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and/or comparable expertise (e.g from industry). Demonstrated expertise in omics data analysis, for example with (meta)genomics, transcriptomics, proteomics, metabolomics, single cell and/or genotyping
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) should contain the following documents: a 1-page summary of your motivation for the position, your research experiences and future goals a detailed CV including publication list contact information of 2-3
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-throughput characterization of the biophysical properties of natural and synthetic protein nanopores and (ii) leverage the generated data for the development of better computational design methods based