Post-Doctoral Fellow

University of Colorado • Aurora, CO

2d • Hybrid

About The Position

How can we turn the vast and rapidly growing collections of publicly available biological data into reusable engines for discovery? And how do we build the software infrastructure that makes that reuse possible at scale — not just once, but sustainably, across the research community? The Davis and Krishnan labs work at the intersection of these questions. We develop ML/AI methods and open-source software and data infrastructure that make massive public biological data collections reusable and turn them into platforms for biological discovery. A central theme in our groups is the development of transferable learned representations from large-scale public omics data and metadata, and the software infrastructure needed to build, maintain, and distribute them at community scale, including through active contributions to the Bioconductor ecosystem.

THE POSITION: We are seeking a postdoctoral researcher who is excited to engage deeply with the scientific questions that animate our groups and help push them forward in creative and impactful ways. This is not a narrowly defined project-based position. Instead, we are looking for someone who is interested in contributing to the broader intellectual life of the labs while developing their own research directions. Postdocs in our groups are encouraged to propose new ideas, shape research directions, and participate actively in the scientific discussions that drive the lab’s work. We also see postdocs as important mentors and role models for graduate students in the Krishnan lab, helping elevate the scientific thinking and rigor of the group. Through the Davis lab, the postdoc will also join a multi-site collaborative group of Bioconductor developers to help drive informatics tooling for cancer genomics.

WHAT WE’RE LOOKING FOR | We are particularly interested in candidates who:
● Think deeply about scientific questions and enjoy identifying important problems
● Demonstrate intellectual independence and initiative
● Value careful reasoning, rigorous analysis, and clear scientific communication
● See software as a scientific contribution that enhances research
● Enjoy collaborative science and exchanging ideas across projects
● Are interested in mentoring and supporting graduate students
● Can translate ideas into finished scientific work, including the potential for published software as well as papers

The most successful postdocs in our groups tend to be those who enjoy proposing new ideas, questioning assumptions, helping push the scientific conversation forward, and who find satisfaction in turning their ideas into useful tools.

Our labs aim to create an environment where postdoctoral researchers can do their best work and grow into independent scientific leaders in academia or industry. Postdocs are treated as emerging colleagues and intellectual partners, with opportunities to develop and lead research directions aligned with the lab’s broader themes. Working across both groups, a postdoc can engage with the full stack of modern computational biology: from data engineering and large-scale infrastructure to method development and biological interpretation. They will have access to established open-source software ecosystems with real user communities, as well as the methods expertise and collaborative environment of an active computational biology group. We emphasize thoughtful scientific discussion, intellectual generosity, and rigorous analysis — and we believe that ML methods development and software infrastructure are equally important scientific contributions.

Requirements

• PhD (or equivalent) in Computational Biology / Bioinformatics, Biomedical Informatics, Computer Science, Statistics / Biostatistics, Applied Mathematics, or a closely related discipline
• Demonstrated research productivity, evidenced by ≥1 first-author peer-reviewed publication or preprint in a relevant area (computational biology, ML, genomics, etc.)
• Strong computational background, including:
• 2 years of experience in programming (e.g., Python, R, Julia, or similar);
• Evidence of working with large-scale datasets (e.g., genomics, transcriptomics, or other high-dimensional data);

Nice To Haves

• ≥2 first-author publications or a strong publication record in relevant areas
• Experience developing or applying advanced ML/AI methods (e.g., deep learning, generative models, representation learning)
• Experience working with large public biological datasets/repositories (e.g., GEO, SRA, UK Biobank, GTEx, etc.)
• Demonstrated experience in method and tool development (e.g., new algorithms, tools, or computational frameworks)
• Evidence of interdisciplinary research, bridging computational and biological domains
• Prior experience mentoring junior researchers (e.g., listed supervision, co-authorship patterns)
• Evidence of collaborative research, including multi-author or cross-group projects
• Experience with machine learning, AI, statistical modeling, or data analysis, demonstrated through publications, projects, or dissertation work
• Track record of completing research projects, as evidenced by publications, preprints, or clearly defined completed work on CV and Google Scholar
• Contributions to open-source software, reproducible pipelines, or publicly available tools (e.g., GitHub repositories)

Responsibilities

• Lead and develop research projects that make massive public biological data collections reusable and turn them into platforms for biological discovery
• Propose and pursue new scientific ideas that align with and expand the two labs’ research directions, particularly developing novel ML/AI methods and open-source software and data infrastructure
• Design, implement, and evaluate computational methods and software for integrating and analyzing large-scale public omics data and metadata
• Translate research into publications, including leading manuscripts and contributing to collaborative projects
• Translate research into the software infrastructure needed to build, maintain, and distribute the methods and data/metadata representations at community scale
• Engage actively in the intellectual life of the labs, including contributing to discussions, providing feedback, and shaping scientific directions
• Mentor graduate students and contribute to a collaborative, supportive research environment
• Collaborate with internal and external partners, including interdisciplinary collaborators across computational and biomedical domains

Benefits

• The University of Colorado offers a comprehensive benefits package.
• To see what benefits are available for Post-Doctoral Fellows, please visit:
• Payroll & Benefits Orientation for Post-Doctoral Fellows | University of Colorado (cu.edu)
• benefits guide cover-post-doc-2024 (cu.edu)

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree