Duke University
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Postdoctoral Associate Duke University in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Deadline
Apr 20, 2027
Country
United States
University
Duke University
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About this position
This postdoctoral associate position at Duke University supports the completion of a recently funded project focused on kidney health in study cohorts from Eastern North Carolina.
The project has already completed its field-based phase, and biological specimens are being transferred to Duke for laboratory processing and analysis. The successful candidate will work with human urine and blood specimens, carrying out molecular processing and downstream analysis that connects specimen-derived data with measures of kidney health and environmental exposures.
The role is centered on multi-omics biomarker discovery and analysis. The postdoctoral associate will help extract and assess DNA, RNA, proteins, and metabolites; prepare samples for transcriptomic, proteomic, and metabolomic profiling; and analyze established and emerging biomarkers of kidney injury and kidney function.
On the computational side, the position emphasizes statistical and machine-learning analysis of transcriptomic, proteomic, metabolomic, and clinical datasets. The work includes biomarker discovery, pathway and enrichment analysis, multivariate modeling, feature selection, prediction, clustering, and integration of molecular, clinical, and exposure data.
The appointee will collaborate closely with study investigators and multidisciplinary collaborators, maintain reproducible workflows and research records, interpret molecular findings, and contribute to reports, presentations, manuscripts, and grant deliverables.
The project is tied to a recently awarded grant and has a one-year timeline with the possibility of extension.
Funding details
Available
What's required
Demonstrated expertise in kidney health, molecular analysis of human urine and blood specimens, transcriptomics, proteomics, metabolomics, and high-dimensional omics data analysis is sought. The candidate should have experience with normalization, quality control, differential expression or abundance analysis, multiple-testing correction, pathway and functional enrichment analysis, biomarker discovery, and integration of omics with clinical/phenotypic data. Experience with computational and machine-learning approaches such as supervised and unsupervised learning, dimensionality reduction, clustering, feature selection, predictive modeling, and cross-validation is particularly desirable.
How to apply
Apply through the Academic Jobs Online application page. Submit the materials requested on the posting before the deadline.
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Prepare your application
Review drafts and confirm requirements with the university.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.