Postdoctoral Associate - Molecular Biology and Genetics

Position Function

The successful candidate will lead the development of novel AI frameworks that:
(i) integrate thousands of genome assemblies and multimodal biological datasets to uncover the genomic determinants of species resilience, adaptation, and extinction vulnerability, and
(ii) translate these genomic insights into actionable, decision-ready strategies for conservation.

Two central goals of GAIA are:

• To build an AI Genome Curation Assistant (Jarvis): an AI system that combines modern machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks to minutes thus that allowing to scale up to thousands of species per year;
• To build Genera, a multimodal AI system that integrates genomic and ecological data to assess extinction risk and generate actionable genetic rescue strategies for conservation practitioners.

The Postdoctoral Associate will primarily focus on the development of Genera, with opportunities to collaborate closely with the Jarvis development team and broader GAIA partners.

Anticipated Division of Time

80% Research: Build, train, and evaluate cutting-edge AI models and multi-agent systems for conservation genomics; perform feature engineering using chromosome-level genome assemblies.
15% Writing: Contribute to scientific manuscripts, progress reports, and collaborative research efforts.
5% Professional Development.

Requirements

• PhD Degree in computational biology, bioinformatics, computer science, electrical engineering, or a related field.
• 3+ years of experience achieving impactful results including publications using relevant AI/ML/related techniques and genomic data analysis.
• Background in and motivation for genomics or/and biodiversity conservation studies.
• Strong background in AI/ML fundamentals and extensive experience with deep learning (DL) methods.
• Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets.
• Experience with sequence modeling architectures and interpretable AI methods (SHAP, Integrated Gradients, etc.).
• Demonstrated ability to stay up to date with advances in AI and apply new methods to research problems.
• Independent, highly motivated, and collaborative researcher able to work effectively with multidisciplinary teams.

Supervision Exercised

This position involves close collaboration with bioinformaticians, genomic consortia, and multidisciplinary research teams. Functional supervision may be exercised over graduate and undergraduate students involved in research projects as needed.

To apply:

Qualified candidates should submit a short cover letter, curriculum vitae, and contact information for three references via the website.

Review of applications will begin immediately and continue on a rolling basis until the position is filled; applicants are encouraged to apply early for full consideration.

Pay Range:

$62,232.00 - $88,745.00