About the position
Position Description
This position will support the completion of a recently funded research project investigating kidney health in study cohorts from Eastern North Carolina. The field-based component of the project has been completed, and biological specimens are now being transferred to Duke University for laboratory processing and analysis. The project requires an individual with specialized expertise in kidney health, molecular analysis of human urine and blood specimens, and the analysis and integration of multi-omics datasets.
The successful candidate will be responsible for processing, preparing, and analyzing urine and blood samples collected from study participants. Responsibilities will include the extraction and quality assessment of DNA, RNA, proteins, and metabolites; preparation of samples for downstream molecular and biochemical analyses; and implementation of rigorous quality-control procedures. The position values expertise in kidney health biomarker discovery, validation, and analysis, including established and emerging urinary and blood-based biomarkers of kidney injury and function.
Importantly, demonstrated expertise in transcriptomics, proteomics, and metabolomics is sought for this position. The successful candidate should have experience with the statistical analysis of high-dimensional omics datasets, including appropriate approaches for normalization, quality control, differential expression or abundance analysis, multiple-testing correction, pathway and functional enrichment analysis, and biomarker discovery. Expertise in integrating transcriptomic, proteomic, metabolomic, and clinical/phenotypic data using computational and machine-learning approaches is particularly desirable. Relevant experience may include supervised and unsupervised learning, dimensionality reduction, clustering, feature selection, predictive modeling, cross-validation, and multi-omics data integration.
The individual will work closely with study investigators and collaborators to connect molecular profiles with measures of kidney health and environmental exposures, with the goal of identifying biomarkers, molecular pathways, and biological signatures associated with kidney injury and disease. Responsibilities will include maintaining reproducible computational workflows, performing statistical and machine-learning analyses, integrating molecular and clinical datasets, interpreting findings, and contributing to scientific reports, presentations, and manuscripts.
This position is essential to fulfilling commitments associated with the recently awarded grant. The project has a one-year timeline with the possibility of extension.
The primary duties of this position include:
Key Responsibilities
10% - Process, aliquot, and prepare human urine and blood specimens for laboratory and multi-omics analyses. Extract and assess the quality and quantity of DNA, RNA, proteins, and metabolites from biological specimens. Prepare and/or coordinate samples for transcriptomic, proteomic, and metabolomic profiling.
20% - Measure and analyze established and emerging biomarkers of kidney injury and kidney function.
40% - Perform statistical analysis of transcriptomic, proteomic, metabolomic, and clinical datasets. Conduct differential expression/abundance, pathway, enrichment, and biomarker discovery analyses. Integrate multi-omics, kidney health, environmental exposure, and clinical/phenotypic datasets. Apply machine-learning and multivariate statistical approaches for feature selection, classification, prediction, clustering, and identification of molecular signatures associated with kidney health outcomes.
10% - Develop and maintain rigorous, reproducible computational and bioinformatic workflows. Implement quality assurance and quality-control procedures for both biological specimens and resulting molecular datasets. Maintain accurate specimen inventories, laboratory records, analytical documentation, and research databases.
20% - Interpret molecular and computational findings in collaboration with project investigators and multidisciplinary collaborators. Contribute to scientific reports, presentations, manuscripts, and other grant deliverables.
Term: 1 year, fulltime
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