Associate Research Scientist; -Throughput Perturbations

Core responsibilities

• Develop and optimize pooled CRISPR-knockout, CRISPRi/a or base-editing libraries in primary tumor and immune cell lines

• Integrate single-cell read-outs with custom barcoding and multiplexing strategies

• Coordinate with software teams to stream raw data into real-time agent analysis loops and feed validated results back into model re-training cycles

Maintain rigorous QC, automation, and data-management pipelines; co-author manuscripts and mentor trainees

Appointment jointly housed in Columbia's Irving Institute for Cancer Dynamics and The Fu Foundation School of Engineering & Applied Science. You will collaborate daily with a diverse team of AI researchers, computational biologists, clinicians, and bioengineers who share a mission of transforming our understanding of metastatic progression through next-generation AI and experimental platforms.

• Ph.D. or equivalent experience in Molecular Biology, Bioengineering, Genomics, or related field (candidates with exceptional M.S. + extensive screen experience will be considered)

• Demonstrated track record running high-throughput CRISPR screens and downstream single-cell library prep. Familiarity with NGS, flow-based cell sorting, and standard mammalian tissue-culture techniques

• Competence in scripting (Python/R) for basic data processing; willingness to learn advanced analytics

Experience with co-culture systems, organoid or organ-on-chip assays

Knowledge of statistical design of experiments, multiplexed imaging or spatial-omics

Columbia University is an Equal Opportunity Employer / Disability / Veteran

Pay Transparency Disclosure

The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University s good faith and reasonable estimate of the range of possible compensation at the time of posting.