Scientist, Single Cell Technology Development & Translational Genomics - (Immunogenomics Group - Biohub NY)

The Team

The Chan Zuckerberg Biohub Network (https://www.czbiohub.org/) is a group of nonprofit research institutes that bring together scientists, engineers, and physicians with the goal of pursuing grand scientific challenges on 10- to 15-year time horizons. The CZ Biohub Network focuses on understanding underlying mechanisms of disease and developing new technologies that will lead to actionable diagnostics and effective therapies.

 

 

Our Vision

• We pursue large scientific challenges that cannot be pursued in conventional environments
• We enable individual investigators to pursue their riskiest and most innovative ideas
• The technologies developed at the CZ Biohub Network facilitate research by scientists and clinicians at our home institutions and beyond

Diversity of thought, ideas, and perspectives are at the heart of CZ Biohub Network and enable disruptive innovation and scholarly excellence. We are committed to cultivating an organization where all colleagues feel inspired and know their work makes an important contribution.

 

The Opportunity

The Laboratory of Immunogenomics at CZ Biohub NY studies the non-coding regulatory genome to understand and address immune dysfunction in diseases like cancer, autoimmune disorders, and aging. We focus on enhancers—non-coding, highly cell–type–specific transcriptional regulatory elements—and their role in shaping immune responses. We develop and utilize genomic technologies, including bulk and single-cell nascent RNA sequencing, genome editing, immune engineering, and CRISPR-based functional screens in patient biopsies, organoid systems, and mouse models. Through computational analysis integrating machine learning and AI, we map enhancer–gene networks and identify disease-driving elements. Our goal is to advance enhancer-guided precision genomic medicine for diseases involving immune dysfunction.

We are seeking a highly motivated Scientist to lead the development of a high-throughput adaptation of a newly established single-cell nascent RNA sequencing method (scGRO-seq) and apply it to patient biopsies and organoid models. The role involves designing, optimizing, and benchmarking cutting-edge single-cell genomics workflows, integrating them with perturbation-based assays, and generating datasets that drive transformative insights into immune regulation.

For candidates with demonstrated technical expertise and leadership ability, this position offers the opportunity to build and lead a small team (one to two research associates) to implement the assay, conduct perturbation experiments, and oversee data generation. Once the workflow is established, this position will direct experimental execution, coordinate large-scale data acquisition, and collaborate on downstream analyses with computational biologists to deliver actionable translational insights.

This role is ideal for a post-PhD scientist with a proven track record in single-cell biology who excels at the intersection of technological innovation, complex biology, and computational analysis. You will join a highly collaborative team working toward a shared vision. The position offers rich opportunities for professional growth—developing leadership, mentorship, and multidisciplinary expertise—equipping the candidate to thrive at the cutting-edge of single-cell immunogenomics for future academic or industry roles.

What You'll Do

• Assay development: Design, optimize, and scale high-throughput single-cell/nuclei extraction, barcoding, and library preparation workflows for scGRO-seq.
• Perturbation integration: Combine scGRO-seq with CRISPR-based enhancer perturbation, drug treatments, and multi-omics profiling.
• Molecular biology techniques: Execute advanced methods including assay design, cell manipulation, RNA/DNA isolation, PCR/qPCR, NGS library construction, high-dimensional flow cytometry, and sequencing.
• Translational applications: Adapt and validate protocols in patient biopsies and 3D organoid models, with emphasis on reproducibility and sensitivity.
• Immune cell engineering: Isolate, culture, and reprogram hematopoietic stem/progenitor cells (HSPCs) and primary immune subsets.
• Data integration: Partner with computational biologists to process and interpret large-scale single-cell datasets (RNA, chromatin, proteomics) using established