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of neurodegeneration, totaling to over 1,500 human brain samples. Using an array of -omics techniques, e.g. long read DNA sequencing, single nuclei transcriptomics and multi-modal proteomics, the team aims to identify
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to mentoring junior lab members and participate in grant writing and dissemination activities Develop and execute strategies for cloning, genotyping, RNA sequencing, stress assays Contribute to a collaborative
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disease. Key responsibilities Lead and conduct the processing and statistical analysis of large-scale long-read RNA and DNA sequencing, single nuclei RNA sequencing and spatial transcriptomics
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functional screens and read out mutation identities via barcode sequencing. By providing a direct link between sequence and function MAGESTIC enables massively scaled screens of precise mutations, with
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for identification of targets for early detection, prevention or treatment of Alzheimer’s disease. Position Lead and conduct the processing and statistical analysis of large-scale long-read RNA and DNA sequencing
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functional screens and read out mutation identities via barcode sequencing. By providing a direct link between sequence and function MAGESTIC enables massively scaled screens of precise mutations, with
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or treatment of Alzheimer’s disease. Position Lead and conduct the processing and statistical analysis of large-scale long-read RNA and DNA sequencing, single nuclei RNA sequencing and spatial transcriptomics
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aligning research proposals with the LINES research group, and develop the postdoctoral researcher’s own research agenda. The ideal candidate will: Contribute to ongoing projects and develop new lines
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short- and long-read whole genome sequencing datasets. This position provides the opportunity to develop and apply advanced bioinformatics tools and conduct innovative analyses, including integrating long
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models for multivariate extremes) proposed a novel modelling framework based on regular vine tree sequences called X-vines. These models can easily be built in arbitrary dimensions by combining bivariate