Deep Learning Proteomics Postdoc Jobs (NOW HIRING)

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 nominate therapeutic avenues.

• Contribute to genotype-first and cohort-based studies that connect deeply phenotyped patients to population-scale resources such as biobanks and reverse-phenotyping pipelines.

• Work with clinical data alongside molecular data to link genetic and proteomic findings to patient phenotypes and outcomes.

• Collaborate with clinical teams, genetic counselors, laboratory scientists, and external partners to move findings into clinical or experimental follow-up.

• Present genetic findings and variant interpretations to internal and external groups and take part in multidisciplinary case and research conferences.

• Communicate results through manuscripts, presentations, case reports, and methods papers, and publish routinely.

• Establish and maintain productive internal and external scientific collaborations.

• PhD, MD, or equivalent in a relevant field such as genetics, genomics, molecular biology, bioinformatics, computational biology, data science, or a related discipline.

• Genuine depth in one of the two core areas below, together with the aptitude and eagerness to grow into the other.

• A real interest in translational, patient-focused discovery and comfort interpreting wet-lab and computational findings.

If you come from genetics and molecular biology:

• Strong grounding in disease biology, functional genomics, or molecular mechanism.

• Some informatics or programming training (e.g., scripting, sequence analysis, working with sequencing data) and the drive to deepen it.

• Experience with variant classification and ACMG/AMP interpretation is a strong plus.

If you come from data science:

• Strong quantitative and computational skills applied to biological, clinical, or other complex, high-dimensional data.

• Some molecular biology or genetics training and the drive to deepen it.

• Experience with genomic, proteomic, or other multi-omics datasets is a strong plus.

• Experience integrating proteomic data (e.g., Olink) with genomic data, and a track record of reasoning across -omic layers.

• Familiarity with AI/ML methods for multi-omics integration, biomarker or phenotype discovery, or extracting signal from clinical data.

• Prior work with population-scale or biobank cohorts, genotype-first study designs, or reverse phenotyping.

• A strong publication record and the writing habits to sustain one.