Description
Conduct research using computational biology, statistics, and algorithm development to model and analyze biological systems across genomics, transcriptomics, proteomics, and other omics. Build reproducible pipelines, curate datasets, and collaborate with experimental and clinical teams; may design databases and develop algorithms for processing and interpreting genomic and other biological information.
- • Recommend computational workflows and processes to improve research efficiency and reproducibility.
- • Stay current with computational biology methods, omics technologies, and software via literature and conferences.
- • Partner with experimental, clinical, and data teams to plan studies and refine analyses.
- • Collaborate with software engineers and scientists to develop and optimize analysis pipelines.
- • Evaluate and benchmark new and updated computational biology tools.
- • Provide statistical models and computational methods for genetic, expression, and functional analyses.
- • Generate cohort-level and population-level summary statistics from genomic and other omics data.
- • Train researchers on the selection and use of computational biology tools and pipelines.
- • Improve usability and visualization in analysis platforms and data portals.
- • Lead or mentor analysts and engineers applying computational methods to proteomics, transcriptomics, metabolomics, or clinical data.
- • Develop and maintain analysis code, workflows, and reproducible pipelines.
- • Design data schemas and build databases for biological datasets.
- • Build or adapt web-based analytical and visualization tools.
- • Design and apply algorithms, including machine learning, dynamic programming, graph algorithms, or Bayesian models.
- • Create novel computational approaches and analytical tools to meet research goals.
- • Aggregate, curate, and harmonize datasets for gene expression profiling, genome annotation, phylogenomics, or structural biology.
- • Communicate findings through presentations, publications, and project reports.
- • Integrate and query public and proprietary omics databases and knowledge bases.
- • Consult with collaborators to define problems, recommend technology solutions, and select computational strategies.
- • Analyze large-scale molecular and phenotypic datasets for basic, translational, or clinical research.
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Last reviewed: Jan 2026
