Postdoctoral Fellow (Bioinformatics and Computational Systems Biology)

Our research group develops multi-scale computational and statistical frameworks for modeling, visualizing, and integrating dynamic perturbation and environmental exposure effects in complex biological systems. Our ongoing research spans cancer biology, immune responses, drug interactions, environmental health, and reproductive biology, with the overarching goal of advancing personalized and precision medicine. We maintain strong collaborations with both national and international partners. Successful candidates will develop and apply advanced computational models, including AI/ML methods that integrate multi-omics data, including genomic, transcriptomic, epigenomic, proteomic, and microenvironmental measurements, to identify mechanistic insights, predict disease progression, and anticipate treatment response. A major focus of our program is maternal-fetal health by way of leveraging human placenta studies to understand how environmental and genetic factors influence genomic regulation, immune dynamics, and disease susceptibility during pregnancy. Our projects involve the integration of genomic ancestry, high-resolution single-cell, and spatial sequencing data to characterize maternal-fetal communication and improve patient-specific predictive modeling. More information about our group can be found here: https://www.niehs.nih.gov/research/atniehs/labs/bcb/staff/anchang

Prospective candidates should have completed, or be close to completing, a Ph.D. in biostatistics, statistics, bioinformatics, computational biology, computer science, genetics, molecular biology, or a closely related quantitative life science field.

Required qualifications include:

• Strong programming skills and experience working with large-scale biological datasets
• Proficiency in one or more of the following: R, Python, MATLAB, Linux/Unix, Shell scripting, and/or Java
• Solid background in computational analysis of population genomics and next-generation sequencing data, including Genome-Wide Association Studies (GWAS).
• Excellent written and verbal communication skills in English

Preferred qualifications include:

• Experience with single-cell and/or spatial omics analysis, especially in the reproductive and/or endocrine systems
• Familiarity with multi-omics data integration and statistical modeling
• Experience working in high-performance computing environments