ASSOCIATE IN RESEARCH
 Duke Clinical Research Institute | |
 United States, North Carolina, Durham | |
| 300 West Morgan Street (Show on map) | |
 Mar 05, 2026 | |
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School of Medicine Established in 1930, Duke University School of Medicine is the youngest of the nation's top medical schools. Ranked sixth among medical schools in the nation, the School takes pride in being an inclusive community of outstanding learners, investigators, clinicians, and staff where interdisciplinary collaboration is embraced and great ideas accelerate translation of fundamental scientific discoveries to improve human health locally and around the globe. Composed of more than 2,500 faculty physicians and researchers, more than 1,300 students, and more than 6,000 staff, the Duke University School of Medicine along with the Duke University School of Nursing, Duke University Health System and the Private Diagnostic Clinic (PDC) comprise Duke Health. a world-class academic medical center. The Health System encompasses Duke University Hospital, Duke Regional Hospital, Duke Raleigh Hospital, Duke Primary Care, Duke Home and Hospice, Duke Health and Wellness, and multiple affiliations. Summary The position will contribute to two complementary research projects. In the first project, the appointee will develop a computational framework to identify low-quality cells that exhibit cross-modality mismatches in single-cell multi-omics data. In the second project, the appointee will develop a statistical methodology for integrative analysis of multi-sample single-cell multi-omics data. Job Duties The appointee will lead the design and implementation of novel computational and statistical methods for single-cell multi-omics analysis. Responsibilities include developing a robust framework to detect low-quality cells exhibiting cross-modality inconsistencies, as well as creating scalable statistical methodologies for integrative analysis across multiple samples. The appointee will build, document, and maintain associated software packages, curate and preprocess large-scale real-world datasets, and conduct comprehensive benchmarking to rigorously evaluate performance across diverse experimental settings. In addition, the appointee will apply these methods to investigate gene regulatory programs related to aging and cellular senescence, ensuring reproducibility, scalability, and broad applicability of the developed approaches. Minimum Requirements: Master's degree in statistics, biostatistics, or a closely related quantitative field, with demonstrated expertise in genomics, computational data analysis, and scientific programming. Other
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