Biohub Chicago, LLC

Biotechnology

ComputationalBiologist,SpatiotemporalMultiOmics

$130–163k Chicago, Illinois, United States
The Brief

“Computational Biologist, Spatiotemporal Multi-Omics at Biohub Chicago, LLC. Skills: spatiotemporal multi-omics, bioinformatics, machine learning. Develop data analysis workflows for spatiotemporal and multi-omics datasets. Build visualization tools that enable exploration and interpretation of complex biological data across space and time”

What You'll Achieve.

accelerate scientific discovery; curing disease; translating AI capabilities into tools that accelerate research everywhere; understanding underlying mechanisms of disease; developing new technologies that will lead to actionable diagnostics and effective therapies; pursue large scientific challenges that cannot be pursued in conventional environments; enable individual investigators to pursue their riskiest and most innovative ideas; facilitate research by scientists and clinicians at our home institutions and beyond; support biological discovery

Industry & Context.

Biotechnology

What They're Looking For.

Must Have

PhD in bioinformatics, computer science, statistics, computational biology, biomedical engineering or a related quantitative discipline, Minimum 2 years of experience in developing bioinformatic methods, analysis pipelines, or software tools for biological data using appropriate programming language, Minimum of 1 year of experience in at least one of the following areas: spatial data analysis, multi-omics integration, or machine learning for biological applications

Nice to Have

1+ year of experience with imaging mass spec (e. g. , MALDI), 1+ year of experience with single-cell proteomics and/or metabolomics, 1+ year of experience building visualization tools, or scientific software

What You'll Do.

Develop data analysis workflows for spatiotemporal and multi-omics datasets

Build visualization tools that enable exploration and interpretation of complex biological data across space and time

Explore machine learning or transfer learning approaches to support biological discovery

How You'll Work.

Team & Collaboration

Collaborate closely with experimental scientists; contribute to publications, presentations, and open-source software as appropriate; working closely with wet-lab teams

Communication Scope

communicate findings clearly

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