Research Associate

University of Colorado•Aurora, CO

117d•Remote

About The Position

The Linda Crnic Institute for Down Syndrome has an opening for a Bioinformatics Analyst / Data Scientist at the Research Associate level to work at the University of Colorado Anschutz Medical Campus in Aurora. The successful applicant will be an integral member of a collaborative team researching various aspects of Down syndrome using multiple omics-based approaches. Current areas of focus include: The Human Trisome Project (www.trisome.org), a large cohort study designed to understand why individuals with Down syndrome have a different clinical risk profile compared to the typical population. Studying the role of immune dysregulation and hyperactive interferon signaling in Down syndrome. Understanding molecular and genomic features of co-occurring conditions in individuals with Down syndrome. This position is responsible for performing rigorous analysis and integration of multiple -omics datasets in collaboration with other scientists on the team. The successful candidate will have extensive computational and analytical skills and a demonstrated ability perform the tasks outlined below. The duties and responsibilities of the position include, but are not limited to: Working collaboratively with other members of the team to provide high-level, professional, and scientifically rigorous data management and bioinformatic analyses. Developing and implementing complex analysis pipelines, programming, and data visualization techniques for multi-layered -omics datasets with a particular focus on single cell RNAseq and CyTOF analysis of peripheral and tissue-resident innate and lymphoid immune cell populations. Application of these techniques for more broad analysis of the transcriptome, metabolome, proteome, and epigenome will also be expected. Creatively and effectively integrating data from multiple sources to accelerate discoveries. Thinking independently and creatively to identify and implement best-practice bioinformatic and data management solutions. Creating and disseminating tools for all team members to access relevant clinical and sample data. Collaborating with other team members to co-author abstracts, oral and poster presentations, and scientific manuscripts.

Requirements

Ph.D. in Immunology, Bioinformatics, Molecular Biology, Computational Biology, or a related field such as Biostatistics.
Three or more years of experience applying bioinformatic tools to next-generation sequencing data (e.g. RNA sequencing, ChIP-seq, microbiome) and/or related omics-level data.
Strong programming skills in common languages/packages used for bioinformatic and statistical analysis and (e.g. R/Bioconductor/Tidyverse, Python, Perl).
Strong knowledge of statistical principles relevant to biomedical research.
Competency with UNIX-based command line tools and shell scripting.
Experience running and troubleshooting jobs in high performance and/or cloud computing environments.
Ability to analyze and solve complex problems and apply quantitative analytical approaches.
Familiarity with principles of reproducible data analysis and good coding practice.
Proven self-initiative and ability to catalyze new projects or ideas.
Exceptional organizational and time management skills, ability to manage multiple projects and analysis requests.
Excellent written and verbal communication skills, as evidenced by publications and/or presentations.
Experience analyzing and mining clinical data, cancer genomics, and related data sets.
Advanced statistical knowledge as applied to bioinformatics / genomics.

Nice To Haves

Experience working with complex high-dimensional data sets in the field of immunology, including scRNA-seq, CITE-seq, CyTOF, spacial transcriptomics, and spectral flow cytometry of immune cells subsets.
Extensive knowledge of various immune cell subsets including myeloid cells, innate lymphoid cells, T cell subsets, and tissue-resident lymphoid cells.
Strong knowledge of statistical principles relevant to biomedical research.

Responsibilities

Working collaboratively with other members of the team to provide high-level, professional, and scientifically rigorous data management and bioinformatic analyses.
Developing and implementing complex analysis pipelines, programming, and data visualization techniques for multi-layered -omics datasets with a particular focus on single cell RNAseq and CyTOF analysis of peripheral and tissue-resident innate and lymphoid immune cell populations. Application of these techniques for more broad analysis of the transcriptome, metabolome, proteome, and epigenome will also be expected.
Creatively and effectively integrating data from multiple sources to accelerate discoveries.
Thinking independently and creatively to identify and implement best-practice bioinformatic and data management solutions.
Creating and disseminating tools for all team members to access relevant clinical and sample data.
Collaborating with other team members to co-author abstracts, oral and poster presentations, and scientific manuscripts.