The Research Informatics (RI) Bioinformatics group within the University of Minnesota Supercomputing Institute (MSI) is hiring a full-time Bioinformatics Analyst to support research for the Department of Microbiology and Immunology (DMI) at the University of Minnesota. The analyst in this position will conduct cutting-edge bioinformatics analyses in close collaboration with a diverse set of labs within the DMI department. The successful candidate will be the second member of the DMI bioinformatics team, a subset of the nineteen-person RI Bioinformatics group at MSI.

The candidate will work with researchers in DMI on a variety of basic and translational research projects investigating topics including viral immunology, adaptive immune response, pathogen detection and the evolution of drug resistance. Many of these projects involve single-cell sequencing and spatial transcriptomics, so there is ample opportunity for the analyst to break new ground by exploring novel methods and tools. 

MSI and DMI have a strong commitment to the Universityâ™s goal of creating a positive and inclusive campus climate by advancing equity and diversity. We aim to hire high-potential people with varying identities and backgrounds. The analyst in this position must have excellent communication skills, strong analytical, computational, and life sciences training or experience, and be comfortable working independently and as part of an interdisciplinary research team on diverse projects.

As part of an interdisciplinary team, successful candidates will have the responsibility to:
● 80%: Serve as a bioinformatics analyst and developer for research projects for the Department of Microbiology and Immunology (DMI), designing and implementing appropriate informatics workflows.
● 10%: Appropriately document and present methods and results in both internal and external reports, publications, and presentations, and provide periodic seminars and workshops for departmental researchers. 
● 5%: Consult with DMI researchers on experimental design and planning.
● 5%: Identify and integrate new, cutting-edge molecular technologies and analysis methods as they become useful for research purposes (e.g., single cell/nucleus RNA-seq/ATAC-seq, spatial transcriptomics, machine learning, etc.)
 

The ideal candidate will have advanced knowledge of next generation sequencing technologies with demonstrated practical experience in developing and carrying out analytical strategies in the context of NGS applications of critical importance to microbiology and immunology research (e.g. bulk and single cell RNA-sequencing, allele-specific expression analysis, bulk and single-cell ATAC-seq, viral discovery, variant calling, genome assembly and annotation, metagenomics, etc.). While candidates ideally will have experience in more than one type of bioinformatics analysis, candidates are not expected to have experience in all types of analyses we handle and learning new analysis techniques is part of the role.

Required Qualifications
● Doctorate in the life sciences, computer science or engineering, or related field. Candidates must have significant bioinformatics experience.
● 3+ years of research experience in genomics, genetics, molecular biology or computational biology. 
● Hands-on experience in developing and carrying out data analytical strategies and pipelines for analyzing datasets from high-throughput platforms such as next generation sequencing.
● Proficiency with the Linux shell environment, a programming language (R and/or python), plus currently used tools (e.g. BWA, HISAT2, GATK, Seurat, etc.).
● Must be able to understand and translate life scientist researchers' scientific goals into analytical strategies and process requirements.
● Must be able to function as part of an interactive team while demonstrating self-initiative to achieve the project's goals and the group's mission.
● Critical and independent thinking.
● Excellent oral and written English communication and interpersonal skills.

Preferred Qualifications
● Research background in microbiology and/or immunology is highly preferred.
● Significant experience with one or more of the following types of analysis is highly preferred: bulk and single cell transcriptomics, variant calling, genome assembly/annotation and epigenomics.
● Use of reproducible research methods and software version control (e.g. git/GitHub).
● Advanced statistical, data modeling and machine learning skills.
● Experience using high performance computing (HPC) environments and job scheduling.
 

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