- Develop, maintain, and optimize computational pipelines for analysis of single-cell RNA sequencing, single-nucleus RNA sequencing, and spatial transcriptomics datasets.
- Process and analyze large-scale genomic datasets generated from 10x Genomics Chromium, Visium, Visium HD, Xenium, and related platforms.
- Perform quality control, clustering, cell type annotation, differential gene expression analysis, trajectory analysis, data integration, and multimodal analyses.
- Apply machine learning, statistical, and bioinformatics approaches to identify biologically meaningful patterns and generate testable hypotheses.
- Develop reproducible analysis workflows using Linux-based computing environments, high-performance computing resources, and version-controlled code repositories.
- Generate publication-quality figures, visualizations, summaries, and reports for manuscripts, grant applications, presentations, and progress reports.
- Work directly with faculty investigators to interpret results, troubleshoot analyses, and develop data-driven research strategies.
- Assist with management, organization, storage, and archival of large genomic datasets.
- Collaborate with laboratory personnel regarding experimental design, sample preparation, sequencing strategies, and downstream analyses.
- Coordinate data transfer, sequencing submissions, sample tracking, and communication with sequencing and genomics service providers.
- Contribute to preparation of manuscripts, abstracts, presentations, and extramural grant applications.
- Train students, staff, and investigators in computational analysis methods and best practices for genomic data analysis.
- Participate in laboratory meetings, research seminars, and collaborative project discussions.
- May assist with tissue collection, sample preparation, library construction, spatial transcriptomics workflows, and related laboratory activities as needed.
Knowledge, Skills, and Abilities:
- Strong computational and analytical skills with demonstrated experience in biological, genomic, transcriptomic, or other large-scale scientific data analysis.
- Proficiency in Linux/Unix operating systems and command-line environments.
- Experience with Bash scripting and workflow automation.
- Proficiency in R and/or Python programming for scientific computing and data visualization.
- Experience with commonly used single-cell and spatial transcriptomics software packages.
- Knowledge of machine learning, statistical analysis, dimensionality reduction, clustering methods, data visualization techniques and biological data integration approaches.
- Ability to communicate complex computational findings to investigators with diverse scientific backgrounds, and work effectively in a collaborative multidisciplinary research environment.
- Ability to manage multiple collaborative projects simultaneously while meeting deadlines.
- Strong organizational skills, attention to detail, excellent written and verbal communication skills.
