The successful candidate 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 and predict disease progression and treatment response. A major focus of the program is maternal-fetal health, leveraging human placenta studies to understand how environmental and genetic factors influence genomic regulation, immune dynamics, and disease susceptibility during pregnancy. 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.
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:
(1) Strong programming skills and experience working with large-scale biological datasets
(2) Proficiency in one or more of the following: R, Python, MATLAB, Linux/Unix, Shell scripting, and/or Java
(3) Solid background in computational analysis of population genomics and next-generation sequencing data, including Genome-Wide Association Studies (GWAS)
Preferred qualifications:
(1) Experience with single-cell and/or spatial omics analysis, especially in the reproductive and/or endocrine systems;
(2) Familiarity with multi-omics data integration and statistical modeling
(3) Experience working in high-performance computing environments.
Excellent written and verbal communication skills in English are essential.
Interested candidates should submit their curriculum vitae, a detailed statement of their research interests, and the names and contact information for three references to Dr. Anchang Benedict