Postdoctoral Research Fellow — Translational Genomics & Proteomics

Benefits Highlights

• Medical:
  Multiple plan options.
• Dental:
  Delta Dental or reimbursement account for flexible coverage.
• Vision:
  Affordable plan with national network.
• Pre‑Tax Savings: HSA and FSAs for eligible expenses.
• Retirement:
  Competitive retirement package to secure your future.

The Translational Omics Program (TOP) and the DOM‑led ORIGIN initiative at Mayo Clinic are seeking a postdoctoral Research Fellow to help turn molecular data into answers for patients with rare and complex disease. This is a genomics‑first role for a scientist who wants to work where variant interpretation, proteomic signal, and clinical reality meet and who is drawn to the idea that the next gene‑disease link or therapeutic lead may already be sitting in data that hasn’t yet been read the right way.

We are seeking talented individuals and will tailor projects accordingly to background such as a molecular biologist with some informatics training who wants to sharpen their computational skills, or a data scientist with some molecular biology training who wants to point their methods at real disease biology. What matters most is genuine depth in one of these areas, the curiosity to grow into the other, and a real pull toward patient‑focused discovery.

The work is translational and genomics‑centered, and computational and AI‑enabled methods are tools we use to get there rather than the point of the work.

ORIGIN is a bench‑to‑bedside translational research program built to connect multi‑omics discovery with real‑time clinical care across DOM divisions. It draws on deep molecular profiling, functional genomics, and collaborative translational pipelines to sharpen diagnosis, open targeted therapies, and accelerate discovery for patients with serious, complex, and rare disease. Fellows work inside an unusually complete research ecosystem including large patient‑linked biobanks, genome and exome sequencing, plasma proteomics at scale, and direct access to the clinicians, genetic counselors, and laboratory scientists who care for these patients.

Projects evolve with clinical need, emerging discoveries, and collaborative opportunities across the institution, and fellows are encouraged to shape their own. Several directions are especially open right now:

• Integrated omics models. Bringing genomic and proteomic data together into a single framework – including AI/ML approaches – to resolve rare and monogenic disease, rather than reading each –omic layer in isolation.
• New therapies and new associations. Using that integrated view to surface novel therapeutic avenues and establish new gene‑disease associations.
• From the individual to the population. Developing methods and models that bridge the gap between rare monogenic diseases identified in phenotypically selected patients and the role that same variation plays across population‑scale datasets connecting deep‑phenotype discovery with biobank‑scale validation, and back again.

This position provides advanced training in translational omics research and is designed to prepare fellows for independent careers in academia, translational medicine, or industry. Fellows are supported to publish routinely and to build the track record needed to compete for external funding.

Responsibilities are shaped to the fellow’s background and evolving project, so a given fellow will lean into some of these more than others:

• Design and lead translational research projects aligned with ORIGIN priorities, spanning patient identification, precise diagnosis, targeted therapy, and discovery.
• Analyze and integrate multi‑omics data, especially with genomics and proteomics (e.g. Olink) at the core – to investigate disease mechanisms and surface therapeutic opportunities, extending to other –omic layers as projects require.
• Perform variant classification and interpretation following ACMG/AMP guidelines in rare and undiagnosed disease as well as preventive (“healthy screen”) testing and communicate that reasoning clearly to clinical and research audiences.
• Build integrated genomic‑proteomic models, including AI/ML approaches, to elucidate monogenic disease, identify new gene‑disease associations, and…