Postdoctoral Associate | Vertebrate Genome Laboratory

Rockefeller University • New York, NY

7d • Onsite

About The Position

The Vertebrate Genome Laboratory (VGL) at The Rockefeller University seeks a Postdoctoral Associate with expertise in conservation genomics, population genetics, and computational biology to join GAIA (Genomic Artificial Intelligence Applications), a collaborative initiative supported by the Bezos Earth Fund and Google.org. The position focuses on non-human species and applies genomic research to biodiversity conservation and species recovery.

GAIA is developing Jarvis, an AI Genome Curation Assistant intended to detect, interpret, and correct structural errors in genome assemblies, and Genera, a multimodal, agentic AI system intended to integrate genomic, ecological, and other biological data to assess extinction risk and generate genetic-rescue strategies. The successful candidate will contribute population-genetic, evolutionary, and conservation-genomics expertise to guide the technical development, validation, and application of these systems, working closely with AI scientists and engineers across partner institutes.

The research will connect high-quality reference genomes and population-level genomic data to conservation questions and help establish evidence linking genomic information, conservation decisions, and documented recovery outcomes. Potential topics include genetic diversity and inbreeding, demographic history, deleterious variation and genetic load, local adaptation, reproductive failure, disease susceptibility, population viability, and the genomic consequences of conservation interventions. The candidate will have substantial opportunity to shape an independent research program and develop applications across endangered and recovering species.

The position is based at The Rockefeller University in New York City, under the supervision of Drs. Giulio Formenti and Erich D. Jarvis, with close collaboration across GAIA partner organizations. It is expected to be supported for two years, subject to institutional appointment procedures and satisfactory progress.

Requirements

• Ph.D. or equivalent degree in non-human population genetics, conservation genomics, evolutionary genomics, computational biology, bioinformatics, or a related field.
• Strong research experience analyzing genomic variation within or among non-human populations.
• Demonstrated knowledge of population-genetic and evolutionary-genomic theory and methods.
• Experience working with genome assemblies, whole-genome resequencing data, genomic variants, and functional annotations.
• Proficiency in at least one scientific programming language, preferably Python or R.
• Experience developing reproducible computational workflows in Linux and high-performance or cloud-computing environments.
• Strong quantitative, analytical, and scientific-writing skills.
• A record of scientific productivity appropriate to the candidate’s career stage.
• Ability to work effectively in a highly interdisciplinary and cross-institutional collaborative environment.

Nice To Haves

• Research experience in conservation genetics or genomics involving threatened, endangered, managed, or non-model species, and ability to work in large, multispecies projects.
• Experience estimating demographic history, inbreeding, genetic load, effective population size, population structure, gene flow, or adaptive variation.
• Experience integrating genomic data with ecological, environmental, phenotypic, geographic, or life-history information.
• Familiarity with long-read genome assemblies, telomere-to-telomere genomes, genome annotation, comparative genomics, or pangenomics.
• Experience with commonly used population-genomics and variant-analysis tools and workflows.
• Familiarity with conservation-management frameworks, genetic rescue, population viability, or recovery planning.
• Experience collaborating with field biologists, wildlife agencies, zoos, biobanks, conservation organizations, or species-recovery teams.
• Interest in evaluating or interpreting machine-learning models, multimodal datasets, or foundation-model outputs; prior experience developing machine-learning models is welcome but not required.
• Experience communicating complex genomic findings to collaborators without specialized computational or genomics training.
• Experience with systematic reviews, evidence synthesis, meta-analysis, or structured datasets linking conservation interventions to biological or management outcomes.

Responsibilities

• Lead computational analyses in population, evolutionary, and conservation genomics across non-human species.
• Identify biologically meaningful questions, genomic features, and conservation use cases to guide GAIA tool development and evaluation.
• Review and synthesize historical applications of genomic data in conservation, linking genomic evidence, management interventions, and conservation outcomes.
• Develop and apply approaches to quantify genomic diversity, heterozygosity, runs of homozygosity, demographic history, effective population size, genetic load, deleterious variation, population structure, and signatures of selection or adaptation, emphasizing methods interpretable by AI models.
• Integrate chromosome-level reference genomes with population resequencing, functional annotation, ecological, phenotypic, demographic, and conservation-status data.
• Organize, curate, and evaluate genomic datasets used as inputs, training resources, benchmarks, or validation datasets for Genera and related GAIA systems.
• Assess the biological validity, robustness, uncertainty, and interpretability of AI-model outputs.
• Compare conclusions from individual reference genomes with population-level datasets and identify when additional sampling is required.
• Develop conservation-genomics case studies with Revive & Restore, Genera researchers, species-recovery teams, and other conservation partners.
• Translate genomic findings into scientifically rigorous and practically useful interpretations for conservation researchers and practitioners.
• Collaborate with machine-learning scientists, software engineers, genome-assembly specialists, and conservation biologists across participating institutions.
• Contribute to scientific manuscripts, conservation-genomics reports, software and data releases, presentations, and collaborative grant activities.
• Mentor junior researchers and contribute to scientific activities of VGL, GAIA, the Vertebrate Genomes Project, and the Earth BioGenome Project.

What This Job Offers

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