Post-Doc Research Associate

The Brunk Lab at UNC-Chapel Hill seeks a computational postdoctoral researcher to develop and apply methods for integrating single-cell sequencing and imaging data within controlled cell-line model systems. The project focuses on vertical integration of multimodal single-cell datasets, including single-cell DNA copy number, RNA expression, chromatin accessibility, protein abundance, protein localization, and cytogenetic imaging. The postdoctoral researcher will help build computational frameworks that connect sequencing-based measurements with imaging-derived single-cell features, including multiplexed protein imaging and AI-assisted cytogenetic image analysis. This position is central to a funded research program developing single-cell integration frameworks and AI imaging tools to understand how genome structure and molecular state coordinate across individual cells. The grant includes single-cell multi-omics sequencing, 4i protein imaging, CITE-seq validation, and AI-based cytogenetic analysis.

Strong programming skills in Python and/or R. Experience analyzing high-dimensional biological data, especially single-cell sequencing data. Strong statistical and quantitative reasoning skills. Ability to work independently and collaboratively in an interdisciplinary environment. Excellent written and oral communication skills.

Experience with one or more of the following areas is strongly preferred: Single-cell RNA-seq, single-cell ATAC-seq, CITE-seq, single-cell DNA copy number, or multi-omics integration. Computational analysis of microscopy, multiplexed immunofluorescence, spatial/protein imaging, or image-derived single-cell phenotypes. Machine learning, latent variable modeling, variational autoencoders, optimal transport, graph-based integration, or related computational approaches. Experience with tools such as Seurat, Scanpy, scVI, ArchR, Signac, Cell Ranger, Cell Ranger ARC, Harmony, LIGER, MOFA, or related packages. Experience with image-analysis tools or libraries such as CellProfiler, napari, ImageJ/Fiji, scikit-image, Cellpose, PyTorch, TensorFlow, or similar.