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strong asset.•Experience in the analysis of high-dimensional biological data (single-cell or spatial transcriptomics, digital pathology or biomedical imaging) is an advantage.•Comfortable working on Linux
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Genomics (prof. Thierry Voet), the student will furthermore use single-cell transcriptomics tools to identify the genes and gene products that drive AAV replication and packaging across time. Additionally
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approaches to investigate tumour heterogeneity in melanoma. The successful candidate will develop and optimize a comprehensive workflow that integrates spatial transcriptomics, proteomics, and lipidomics
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CRISPR-mediated genetic engineering and pharmacological perturbation approaches. Multi-omics integration (lipidomics, metabolomics, proteomics and transcriptomics) will be used to identify pathogenic
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spatial transcriptome, protein and epigenome readouts from the same tissue section. The project will combine single-cell and spatial multi-omics with DBiT-seq-based spatial omics sequencing technology and