Postdoctoral Associate | Vertebrate Genome Laboratory

Organization Overview

The Vertebrate Genome Laboratory (VGL) at The Rockefeller University leads international efforts in vertebrate genome sequencing, assembly, annotation, and evolutionary analysis. As a core laboratory of the Vertebrate Genomes Project and a major hub of the Earth BioGenome Project, VGL is at the forefront of generating high-quality, telomere-to-telomere genome assemblies across vertebrate diversity.The laboratory provides an end-to-end genomics platform spanning sample processing, sequencing, genome assembly, and downstream data analytics. It specializes in high-molecular weight DNA extraction and long-read genomic technologies, offering integrated library preparation and sequencing services for high-molecular weight genomic DNA, long amplicons, and full-length transcriptome sequencing (Iso-Seq). These capabilities are tightly coupled with in-house computational expertise for genome assembly, curation, annotation, and comparative genomics, and are supported by state-of-the-art platforms including PacBio and ONT.

Overview

We seek a Postdoctoral Associate with expertise in conservation genomics, population genetics, and computational biology to join GAIA-Genomic Artificial Intelligence Applications-a collaborative initiative among The Rockefeller University, Revive & Restore, Cornell University, and other academic and technology partners, supported by the Bezos Earth Fund and Google.org.

GAIA is developing next-generation artificial-intelligence systems that scale genome assembly and enable rapid genome-informed conservation decision making. Its two central goals are:

• To build Jarvis, an AI Genome Curation Assistant that combines machine-learning approaches with large-scale biological data to detect, interpret, and correct structural errors in genome assemblies, substantially reducing the manual effort required to produce high-quality reference genomes.
• To build Genera, a multimodal, agentic AI system that integrates genomic, ecological, and other biological data to assess extinction risk and generate actionable genetic-rescue strategies for conservation practitioners.

The successful candidate is interested in applying genomic research to biodiversity conservation and species recovery and will provide the population-genetic, evolutionary, and conservation-genomics expertise needed to guide the technical development, validation, and application of these systems. The Postdoctoral Associate will work closely with AI scientists and engineers across partner institutes, providing complementary skills and knowledge in biodiversity genomics.

The position will focus solely on non-human species and will connect high-quality reference genomes and population-level genomic data to real-world questions in

biodiversity conservation. The candidate will also help establish an evidence base connecting prior uses of genomic information, conservation decisions, and documented recovery outcomes. Potential research areas include genetic diversity and inbreeding, demographic history, deleterious variation and genetic load, local adaptation, reproductive failure, disease susceptibility, population viability, and predicting the genomic consequences of potential conservation interventions.

The candidate will have substantial opportunity to shape an independent research program within this broad framework and to develop applications across diverse endangered and recovering species.

Responsibilities

Responsibilities include, but are not limited to:

• Lead computational analyses in population, evolutionary, and conservation genomics across non-human species.
• Identify biologically meaningful questions, genomic features, and conservation use cases that can guide the development and evaluation of GAIA tools.
• Review and synthesize historical applications of genomic data in conservation, linking the genomic evidence used, the management interventions undertaken, and the resulting conservation outcomes to inform Genera's development and validation.
• 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, with particular emphasis on methods that can be interpreted by an AI model.
• 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.
• Critically assess the biological validity, robustness, uncertainty, and interpretability of outputs produced by AI models.
• Compare conclusions derived from individual reference genomes with those obtained from population-level datasets and identify when additional sampling is required.
• Develop real-world conservation-genomics case studies in collaboration 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.
• Work closely 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 the broader scientific activities of the VGL, GAIA, the Vertebrate Genomes Project, and the Earth BioGenome Project.

Qualifications

REQUIRED QUALIFICATIONS:

• Ph.D. or equivalent degree in non-human population genetics, conservation genomics, evolutionary genomics, computational biology, bioinformatics, or a related field.
• Strong research experience in the analysis of 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.

PREFERRED QUALIFICATIONS:

• 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 is not required.
• Experience communicating complex genomic findings to collaborators without specialized computational or genomics training.
• Experience with systematic reviews, evidence synthesis, meta-analysis, or the development of structured datasets linking conservation interventions to biological or management outcomes.

The successful candidate will be based at The Rockefeller University in New York City and will work under the supervision of Drs. Giulio Formenti and Erich D. Jarvis, in close collaboration with researchers at Revive & Restore, Cornell University, and other GAIA partner organizations.

The position is expected to be supported for two years, subject to institutional appointment procedures and satisfactory progress.

The Rockefeller University is an equal opportunity employer - veterans/individuals with disabilities. Qualified applicants will receive consideration for employment without regard to characteristics protected by applicable local, state or federal law, including but not limited to disability and protected veteran status.

The salary of the finalist selected for this role will be set based on various factors, including but not limited to organizational budgets, qualifications, experience, education, licenses, specialty, and training. The hiring range provided represents The Rockefeller University s good faith and reasonable estimate of the range of possible compensation at the time of posting.

Compensation Range: Min

USD $72,100.00/Yr.

Compensation Range: Max

USD $72,100.00/Yr.