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| 【有奖交流】积极回复本帖子，参与交流，就有机会分得作者 双子座的秋天 的 19 个金币 ，回帖就立即获得 1 个金币，每人有 1 次机会 | |||
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美国哥伦比亚大学医学院人类衰老单细胞组学方向诚招生物信息学博后
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Position Summary: The Suh Laboratory at Columbia University Irving Medical Center (https://www.yousinsuhlab.org/) is seeking a highly motivated postdoctoral fellow to lead bioinformatics and computational analyses in our research on human ovarian aging. This position focuses on integrating single-nuclei multi-omics (snRNA-seq, snATAC-seq) and spatial transcriptomics to characterize the molecular and genetic mechanisms driving ovarian aging. The successful candidate will develop and apply computational pipelines to analyze high-dimensional datasets, reconstruct aging trajectories, and identify key regulatory drivers and functional non-coding variants associated with reproductive aging. The postdoc will collaborate with wet-lab researchers who will perform experimental validation and contribute to manuscripts, presentations, and grant applications. This position provides an excellent opportunity to work at the intersection of aging biology, reproductive science, and computational genomics, contributing to fundamental discoveries in women’s health and aging research. Job Responsibilities: · Analyze and integrate single-nuclei multi-omics (snRNA-seq, snATAC-seq) and spatial transcriptomics data to characterize the molecular mechanisms of ovarian aging. · Develop and apply computational pipelines for high-dimensional data analysis, including reconstructing aging trajectories and prioritizing functional non-coding variants. · Collaborate with wet-lab researchers to interpret findings and support experimental validation of key regulatory elements. · Contribute to scientific communication, including manuscript preparation, presentations, and grant applications. Minimum Qualifications: · A doctoral degree in biology, bioinformatics, computational biology, or a related field with a focus on aging, reproductive biology, or women's health. · Strong expertise in single-cell omics analysis (snRNA-seq, snATAC-seq, and/or spatial transcriptomics). · Solid understanding of the human genome, specifically related to gene expression patterns and cell biology in the context of ovarian aging. Proficiency in R and at least one other programming language, such as Python or Perl, with experience in handling large-scale biological datasets Ability to work independently and collaboratively in a multidisciplinary research environment. Preferred Qualifications: · Background in ovarian biology, reproductive aging, or aging biology in general. · Experience in integrative multi-omics analysis and statistical modeling of biological systems. · Strong publication record in relevant fields. |
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