Bioinfo-Comput Biologist Lead

Michigan Medicine improves the health of patients, populations and communities through excellence in education, patient care, community service, research and technology development, and through leadership activities in Michigan, nationally and internationally.

Our mission is guided by our Strategic Principles and has three critical components; patient care, education and research that together enhance our contribution to society.

We are seeking a Bioinformatician/Computational Biologist Lead to join our research team to direct and conduct bioinformatics analyses to identify mechanisms underlying neurodegeneration, with an emphasis on amyotrophic lateral sclerosis and the neurologic complications of diabetes and obesity, including peripheral neuropathy and cognitive impairment. Specific tasks include designing and applying workflows to analyze high-throughput multi-omics data, including bulk and single-cell sequencing data, from human and mouse nervous system tissues.

Additional responsibilities include developing computational models of disease progression based on molecular signatures;collaborating with the Bioinformatics Core and collaborating investigators; developing data-handling and mining tools to integrate multiple bioinformatics approaches and datasets; and developing databases and database applications. The individual must be able to w rite and collaborate effectively with other laboratory investigators on grants and publications that detail research findings in high-impact biomedical journals.

An interest in supporting and leading bioinformatics analyses for a fast-paced, cutting-edge neurologic disease research program is required. The Lead will direct the laboratorys computational analysis program, set analytical priorities across projects, and work with fellows and collaborating investigators to move bioinformatics projects forward.

Direct the laboratory's bioinformatics analysis function and serve as its domain expert for computational analysis. Create work plans that prioritize projects, coordinate resources, and track progress across studies.

Design, develop, and conduct bioinformatics analyses in the field of neurodegeneration, with a focus on next-generation sequencing analysis and integration for RNA-Seq, variant detection (exome sequencing, genome resequencing), miRNA-Seq, single-cell RNA-Seq, spatial transcriptomics, epigenomics, metabolomics, and proteomics datasets. Perform thorough quality checks and interpret the results in the context of the biological mechanisms being studied.

Collaborate withkey university core facilities and internal and external investigators. Train molecular biologists on the utility and limitations of bioinformatics technologies and work with fellows and collaborating investigators to ensure bioinformatics analysis consistency and quality.

Develop and maintain reproducible analysis workflows and pipelines (e.g., using workflow managers, version control, and containerized software), and keep them current by evaluating new analysis methods and software tools.

Will be responsible for installing bioinformatics software, purchasing software licenses, managing data storage, and provisioning high-performance and cloud computing resources.

Co-author publications and grant proposals and present results at conferences.

Other duties as assigned

An advanced degree (Masters or PhD) in Bioinformatics or related fields, with over 10 years of experience in high-throughput data analysis, is required.

Must have significant experience in designing transcriptomics studies and performing bioinformatics analyses in gene expression profiling, pathway mapping, biomarker identification, and multi-omics data integration.

A proven publication record in peer reviewed journals in the field is essential.

Experience working in an academic laboratory or industry is required, with demonstrated management and coordination of data analysis workflows and experience supervising or mentoring analysts or trainees.

Specific experience with bioinformatics analysis toolkits (e.g., Bioconductor and R packages, GATK, and single-cell analysis frameworks such as Seurat or Scanpy) and programs (e.g., IGV, IPA) are necessary, and proficiency in R and Python, workflow management tools (e.g., Nextflow or Snakemake), version control (e.g., Git), and Linux-based high-performance or cloud computing environments is required.

The ability to work independently and to interact…