Assistant Professor of Reproductive Sciences

The Suh Lab is seeking an Assistant Professor of Research with expertise in computational genomics, bioinformatics, and biomedical informatics/AI/ML to lead the analysis of large-scale genomic datasets. The candidate will direct the application of contemporary computational methods, including predictive artificial intelligence and machine learning frameworks, to address biological questions related to aging, including reproductive aging, and aging-related diseases. This role requires a leader who can utilize cutting-edge next-generation sequencing (NGS) technologies and integrate diverse data resources to generate and test novel biological hypotheses. The successful candidate will be responsible for the computational strategy across several NIH-funded grants and collaborative projects.   Job Responsibilities:  ​​ Computational Strategy: Lead the development and application of specialized methods for analyzing genomic and epigenomic data, including but not limited to ChIP-seq, RNA-seq, ATAC-seq, and other high-throughput sequencing method, as well as the implementation of custom AI/ML pipelines for pattern discovery. Data Integration & Analysis: Collect and interpret publicly available data, including but not limited to transcriptomic/proteomic databases, genomic annotation hubs, and GWAS datasets from relevant studies using advanced informatics approaches and multi-omics data integration tools. Hypothesis Generation: Generate novel hypotheses from the analysis and integration of internal and external data resources using predictive modeling to drive the lab's research strategies. Grant Writing & Application: Play a central role in the preparation, writing, and submission of grant proposals by providing critical data analyses, developing computational and informaticsresearch plans, and crafting the methodology and results sections for applications. Technical Authority: Maintain comprehensive knowledge of NGS data analysis flows, including but not limited to WGS/WES, scRNA-seq, 4C, and HiC, as well as modern machine learning libraries and AI tools, to ensure the lab remains at the forefront of the field. Independent Leadership & Mentorship: Exhibit independent thinking in a highly dynamic environment and provide guidance to junior lab members on computational and informatics/AI/ML methodologies. Minimum Qualifications: PhD in bioinformatics, computational biology, biology, biomedical informatics, computer science, or another appropriate field. Extensive experience with NGS data analysis and proficiency in R , Python, and one or more other programming languages commonly used in data science and machine learning applications.  A distinguished record of high-impact publications and a proven track record of independent scientific thinking and creative problem-solving. Deep knowledge of the human genome, gene expression, cell biology and machine learning architectures. Preferred qualifications: Prior experience or a strong interest in aging-related research and biology. Demonstrated experience applying deep learning or machine learning models to large-scale biological datasets (e.g., single-cell multi-omics or predictive clinical genetics. The Department of Obstetrics and Gynecology is dedicated to the goal of building a multicultural faculty and staff committed to teaching, working and serving in a diverse community, and strongly encourages applications from candidates of traditionally underrepresented backgrounds.  We are continuously seeking to recruit individuals who will enhance the diversity of our workplace and the effectiveness of our organization. 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.