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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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, fundamental and strategic plant biology research is conducted on wild species, agricultural crops, forest trees, bioenergy crops, and model organisms. Our main research areas include genome analysis
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, volumetric analysis, and modeling of structural heterogeneity in biological macromolecules. Rather than only applying established workflows, you will explore new computational formulations and alternative ways
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they are transmitted through populations. Research will have a strong focus on computational analysis or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale
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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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credits, or Mandatory requirement for English equivalent to English B/6 Experience in formulating lipid nanoparticles with mRNA via microfluidics Experience in sample preparation and data analysis from