div class="posting-requirements plain-list">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.
Lead end-to-end TF discovery for cellular reprogramming - from multi-platform single-cell genomics analysis (scRNA-seq, ATAC-seq) through GRN inference, differential analysis, and trajectory mapping - to nominate the regulators that flip cell fate.