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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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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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integration (up to 3 million cells) using deep learning-based approaches, hierarchical clustering, and cell type annotation benchmarked against published CRC atlases Deconvolution and TME characterization
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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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Referensnummer IFM-2026-00053 Work assignments This PhD position focuses on methodological and computational development in cryo-electron microscopy (cryo-EM), with emphasis on image reconstruction
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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