Develop methods and workflows to construct robust co-regulation networks from large single-cell and spatial transcriptomics datasets * Integrate ontologies and metadata (e.g., tissue, cell type, developmental stage, treatment) to build tissue- and context-specific co-regulation networks * Design and implement clustering and integration approaches (e.g., network-based and subspace clustering) * Use co-regulation networks for gene function and protein-protein functional relationship prediction (guilt-by-association), and benchmark them against existing bulk co-expression resources * Prior experience with single-cell data analysis, network analysis, or machine learning is a plus - Further information on doctoral degrees at Forschungszentrum Jülich (including its various branch offices) is available at welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, ...
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