Degui Zhi

Yale BIDS / Zhi Lab is recruiting a postdoctoral researcher to work with Professor Degui Zhi on method-driven genomics and pangenomics. The position is centered on statistical genetics , pangenomics , structural variation , large-scale human genomics , and related computational methods for population-scale analysis. The lab’s research includes large-scale IBD detection, PBWT-based haplotype algorithms, pangenome indexing, relatedness inference, ROH analysis, local ancestry inference, and cross-population haplotype analysis. Current and recent projects also span biobank-scale genomics and access to major cohorts such as UK Biobank , All of Us , ADSP , and HPRC . The broader lab environment at Yale also includes biomedical AI, EHR deep learning, and imaging genetics. Ideal candidates have a PhD in bioinformatics, computational biology, biomedical informatics, computer science, statistics, biostatistics, human genetics, or a related quantitative field. Strong programming skills in Python , R , or C++ are valued, along with experience analyzing large genomic or biomedical datasets. The post is especially suited to applicants who want to focus on the application side of method development while understanding the underlying methodology. Funding is described as a competitive postdoctoral salary aligned with NIH NRSA standards plus Yale benefits , with access to strong computational infrastructure and multi-year NIH-supported projects. Applications are reviewed on a rolling basis until the position is filled. To apply, email one combined PDF to degui.zhi@yale.edu with the subject line Postdoctoral Application - Your Name . Include a 1–2 page cover letter, CV with publication list, and contact information for three references.

Yale University’s Zhi Laboratory in the Department of Biomedical Informatics and Data Science is recruiting a postdoctoral associate in biomedical informatics and data science. The position sits at the intersection of computational genomics, clinical artificial intelligence, and imaging genetics, with opportunities to work on large-scale genomic, clinical, and imaging biobank data. The lab’s research spans computational genomics and pangenomics, including PBWT/GBWT-based haplotype algorithms, identity-by-descent detection, pangenome indexing, and population-scale haplotype analysis; EHR deep learning and clinical AI, including Med-BERT, CovRNN, multimodal representation learning, and trajectory modeling; and imaging genetics, including unsupervised deep imaging phenotyping, neuroimaging GWAS, Alzheimer’s biomarkers, retinal imaging genetics, and multi-omics integration. Applicants should have a Ph.D. completed or expected in Bioinformatics, Computer Science, Statistics, Computational Biology, Biomedical Informatics, or a related quantitative field. Strong Python and/or C++ programming skills, a publication record appropriate to career stage, and the ability to lead independent research are expected. Experience with large biomedical datasets, deep learning, population genetics, pangenome graphs, or GPU/high-performance computing is highly desirable. The appointment is full-time, with a start date in Fall 2026 (negotiable). Funding is supported by multi-year NIH awards, and salary is competitive and aligned with NIH NRSA standards with Yale benefits. Applications are reviewed on a rolling basis until the position is filled. To apply, email a single combined PDF to degui.zhi@yale.edu with a cover letter, CV including publications, and three references. The position is based at Yale University in New Haven, Connecticut, United States.