Job Description

Job Summary
The Center for Statistical Genetics (CSG) in the Department of Biostatistics at the University of Michigan seeks a highly motivated Bioinformatics Scientist to join a collaborative team supporting large-scale genomic research within the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and related studies.

You will contribute to the analysis of large-scale whole-genome sequencing datasets, including both short-read and long-read sequencing technologies, and will play a key role in the development and application of computational approaches for single-cell and bulk omics studies. This position offers you the opportunity to work with one of the world's largest collections of human genomic and phenotypic data while collaborating with investigators across genetics, genomics, epidemiology, biostatistics, and computational biology.

We are especially interested in candidates with demonstrated expertise in single-cell RNA-seq analysis and/or whole-genome sequencing analysis. Individuals who have led genomic analyses resulting in publications, developed reusable computational workflows, or worked with large-scale consortium datasets such as TOPMed, All of Us, UK Biobank, or similar resources are strongly encouraged to apply.

We recognize that excellent candidates may bring different combinations of skills and experiences. While no applicant is expected to possess expertise in all areas, experience in one or more of the following domains would be particularly valuable: single-cell transcriptomics, long-read sequencing, large-scale whole-genome sequencing analysis, cloud-based genomics, workflow development, and multi-omics data integration.

The position may be filled at different levels depending on the education, experience, and qualifications of the selected candidate. You will report to the Principal Investigator.

Responsibilities*
Genomic Data Analysis (35%)

Process, quality control, and analyze large-scale whole-genome sequencing datasets generated through TOPMed and related studies
Support analyses of both short-read and long-read sequencing data, including variant discovery, structural variation, haplotype analysis, and emerging applications enabled by long-read technologies
Integrate genomic results with clinical, phenotypic, and epidemiologic datasets
Single-Cell and Functional Genomics (30%)

Analyze single-cell RNA-seq and related single-cell multiomic datasets
Perform cell-type annotation, differential expression analyses, integration across studies, trajectory analyses, and biological interpretation
Develop and maintain reproducible workflows for single-cell data processing and analysis
Pipeline and Software Development (20%)

Develop, maintain, and optimize scalable bioinformatics workflows and analytical pipelines
Implement reproducible computational methods using workflow management systems such as Nextflow, Snakemake, or WDL
Support analyses on high-performance computing and cloud-based environments
Research Collaboration and Scientific Contributions (15%)

Collaborate with faculty investigators, staff scientists, trainees, and external research partners
Contribute to manuscripts, reports, grant applications, and scientific presentations
Remain current with emerging technologies and analytical methods in genomics and computational biology
Required Qualifications*
Ph.D. in Bioinformatics, Computational Biology, Genetics, Biostatistics, Computer Science, Biomedical Informatics, or a related field with 2-5 years of related experience (1-2 years for Intermediate). Candidates with a Master's or Bachelor's degree may be considered with substantial relevant professional experience
Demonstrated experience analyzing high-throughput sequencing data
Strong programming skills in Python, R, or related scientific programming languages
Experience working in Linux/Unix computing environments
Experience developing reproducible computational analyses and workflows
Strong analytical, organizational, and problem-solving skills
Excellent written and verbal communication skills
Ability to work effectively both independently and as part of a multidisciplinary research team