Position OverviewThe Bioinformatics Fellow for Multi-Omics Analysis will conduct comprehensive analyses of multi-omics data generated from organoid systems and corresponding normal tissues. The incumbent will characterize organoid fidelity, identify biomarkers of successful differentiation, and develop computational frameworks for organoid quality assessment, while gaining substantive training in integrative analysis of single-cell and multi-omics data. The work is central to the SOM Center's research objectives, and the position is intended to prepare the Fellow for an independent research career in academia, government, or industry.
Key Responsibilities- Multi-omics analysis. Analyze complex datasets including single-cell RNA sequencing, bulk RNA sequencing, proteomics, and metabolomics data from a range of organoid systems and their tissue counterparts.
- Pipeline development. Develop and implement computational pipelines for data processing, quality control, and statistical analysis, and document them so that analyses are reproducible across the Center.
- Benchmarking. Integrate SOM-generated data with publicly available datasets in order to benchmark organoid characteristics against normal tissue profiles.
- Method development. Develop and refine analytical approaches - including machine learning methods where appropriate - for classifying cellular states and assessing organoid quality, and contribute to standardized assessment protocols.
- Collaboration and interpretation. Work closely with experimental teams to interpret results and guide protocol optimization, and collaborate with other computational staff on cross-modality data integration.
- Dissemination. Contribute to manuscript preparation and present findings at scientific conferences.
Required Qualifications- Education. Doctoral degree (PhD) in bioinformatics, computational biology, biostatistics, or a related quantitative field, completed or anticipated prior to the start of the appointment.
- Single-cell analysis. Extensive experience with single-cell data analysis, including familiarity with tools such as Seurat, Scanpy, or comparable platforms.
- Programming. Strong programming proficiency in R and Python, together with experience in statistical analysis and data visualization.
- Omics workflows. Working knowledge of proteomics and metabolomics data analysis workflows.
Preferred Qualifications- Prior experience analyzing organoid datasets.
- Experience with machine learning approaches for biological data, familiarity with pathway analysis tools, and knowledge of developmental biology principles.
- Experience with high-performance computing environments and version control systems.
Disclaimer: The above description is meant to illustrate the general nature of work and level of effort being performed by individuals assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.
This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate's experience, qualifications, skills, and location.
#IND
Salary Range
$90,000-$100,000 USD
