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spatial immune cell niches in cancer. Advances in high-throughput spatial omics technologies measuring various analytes now capture this complexity in remarkable detail, providing groundbreaking insights
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analyses, version control) is an advantage We attach great importance to personal qualities in this recruitment. As a person, you are analytical, structured, detail-oriented, and committed to high-quality
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, with a particular focus on identifying and characterizing rare endosomal escape events. The tasks include developing, training, and validating deep learning–based models for event detection and vesicle
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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learn experimental and computational approaches to tackle fundamental biological questions with medical relevance using innovative system-wide techniques. You will work on an exciting multidisciplinary