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neurons and breast cancer organoids, together with clinical tissue material, to investigate cancer-nerve contact sites using multiplexed immunofluorescence, single-cell and single-nucleus transcriptomics
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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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infection models and nanotechnologies, immunohistology, transcriptomics, antibody assays, and advanced 3D microscopy. For this, a lightsheet microscope dedicated to the imaging of cleared samples is being
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project focuses on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High
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for the position. Preferred selection criteria Knowledge of, or experience with, bioinformatics and the analysis of large-scale omics data (e.g., proteomics, metabolomics, transcriptomics), including basic
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single-nucleus RNA sequencing (scRNA-seq/snRNA-seq) is an advantage. Knowledge of data analysis methods, including statistical software and transcriptomic analysis pipelines is an advantage. Excellent
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single-nucleus RNA sequencing (scRNA-seq/snRNA-seq) is an advantage. Knowledge of data analysis methods, including statistical software and transcriptomic analysis pipelines is an advantage. Excellent
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(e.g. spatial transcriptomics or proteomics) and imaging data. Computational and theoretical approaches are encouraged only if tightly coupled to experimental design, data generation, and biological