Identify, extract, and analyze use case-driven benchmark sc/snRNA-seq and other single cell omics datasets of healthy, perturbed, and diseased anatomical structures; develop and implement statistically-rigorous cell type matching approaches; collaborate with other team members to develop a FAIR-compliant cell phenotype semantic schema based on OBO Foundry ontologies and an extraction, translation, and loading (ETL) protocol for translating processed assay results, including transcriptional biomarkers produced using standardized machine learning pipelines, and experiment metadata from the datasets selected into standardized semantically-structured assertions about cell phenotypes.
• PhD and/or MD degree in a biomedical science, computer science or related field.
• Skill in using programming languages and environments used in data science, e.g., Python and/or R as well as associated programming libraries, e.g., numpy, scipy, and bioconductor.
• Strong working knowledge of tools for processing and visualizingscientific data, including web-based applications, e.g., Javascript, Python, R/Shiny apps, software tools and algorithms used to process and analyze scRNA-seq data, including CellRanger, scanpy, Seurat, Leiden clustering, and UMAP/tSNE embedding, parametric and non-parametric statistical and machine learning approaches, including random forest and neural networks
Candidates should send the following application materials directly to Dr. Richard H. Scheuermann at rscheuermann@jcvi.org and copy Dr. Virginia Meyer at virginia.meyer@nih.gov :
• Current curriculum vitae
• Cover letter/statement of research interest
• Contact information for three references