Associate Research Scientist (High-Throughput Perturbations)

Join our interdisciplinary team to design and execute large-scale pooled CRISPR and Perturb-seq screens that close knowledge gaps identified by AI tools.

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

Environment & Application

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.

Required qualifications

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

Preferred extras

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.