Portland, Oregon30+ days ago
PhD in Bioinformatics, Computational Biology, or related quantitative field (or MS with 5+ years relevant industry experience); Demonstrated track record applying computational TF ranking and GRN inference to cellular reprogramming problems, transdifferentiation, directed differentiation, or iPSC systems; Multi-platform single-cell RNA-seq expertise: hands-on analysis from at least two different platforms, including platform-specific troubleshooting and quality control; Multi-modal genomics proficiency: ChIP-seq, CUT&RUN, or ATAC-seq analysis including peak calling, differential accessibility, and TF motif enrichment; Hands-on experience with established GRN inference methods to nominate or rank regulators of cell state, beyond literature-curated lists; Experience analyzing pooled perturbation screens (CRISPRa, CRISPR knockout, or barcoded TF overexpression) with single-cell or bulk readouts; Working knowledge of trajectory inference and pseudotime methods for mapping cell state transitions; Strong programming skills in Python and R, with proficiency in Scanpy/Seurat and statistical analysis for high-dimensional data; Comfortable working in a modern computational environment: cloud platforms, workflow managers, containerization, and collaborative version control; Strong publication record and demonstrated cross-functional collaboration with experimental biologists. This role focuses on gene regulatory network inference, differential analysis of single-cell transcriptomics, and computational prioritization of TF cocktails for cellular reprogramming, requiring deep expertise in multi-platform scRNA-seq analysis and transcriptional regulation biology.