Postdoctoral Researcher - AI Biomanufacturing, Hybrid

Position: Postdoctoral Researcher - AI for Biomanufacturing, Hybrid
About Sandia:

Sandia National Laboratories is the nation's premier science and engineering lab for national security and technology innovation, with teams of specialists focused on cutting-edge work in a broad array of areas. Some of the main reasons we love our jobs:

+ Challenging work with amazing impact that contributes to security, peace, and freedom worldwide

+ Extraordinary co-workers

+ Some of the best tools, equipment, and research facilities in the world

+ Career advancement and enrichment opportunities

+ Flexible work arrangements for many positions include 9/80 (work 80 hours every two weeks, with every other Friday off) and 4/10 (work 4 ten-hour days each week) compressed workweeks, part-time work, and telecommuting (a mix of onsite work and working from home)

+ Generous vacation, strong medical and other benefits, competitive 401k, learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance
* World-changing technologies. Life-changing careers. Learn more about Sandia at: http://(Use the "Apply for this Job" box below)..gov

* These benefits vary by job classification.

What Your Job Will Be Like:

Sandia/California's Biomaterials and Biomanufacturing Department is seeking a dedicated and motivated Postdoctoral Scientist (job title: JBEI Postdoctoral Bio/Chem) to work within the Agile Bio Foundry (ABF; agile biofoundry.org), focusing on the development and application of computational tools for engineering non-model fungi toward biofuel and biochemical production. The goal of the ABF is to cultivate the bioeconomy by developing advanced biomanufacturing capabilities based on a Design, Build, Test, Learn cycle that are accessible to industry and academic partners.

Professional scientists and engineers are developing non-model fungal conversion hosts and have engineered pathways for a wide range of bioproducts into them, including terpenes, lipid-derived products, and organic acids. We are seeking an individual with expertise in computational biology to join us to use advance AI tools, Agentic AI, and other computational biology approaches to help develop valuable bioproducts and optimize their titers, rates and yields to industrially relevant levels.

On any given day, you may be called on to:

+ Build and deploy AI/ML models to predict phenotypes, pathway yields, or gene targets for optimization.

+ Analyze multi-omics datasets (transcriptomics, proteomics, metabolomics, lipidomics) to inform strain engineering hypotheses.

+ Develop AI agents to simplify and standardize science workflows.

+ Leverage genome-scale metabolic models (GEMs) to identify metabolic bottlenecks and design strategies for improved strain performance.

+ Collaborate closely with experimental biologists to translate computational predictions into testable strain designs within the design-build-test-learn (DBTL) framework.

+ Apply retro biosynthesis and pathway design tools to identify novel biosynthetic routes for target molecules.

+ Participate in laboratory management and mentoring student interns.

+ Present results at project meetings and conferences.

The selected applicant can work a combination of onsite and offsite work. The selected applicant must live within a reasonable distance for commuting to the assigned work location when necessary.

Qualifications We Require:

+ PhD in Bioengineering, Computational Biology, Systems Biology, Biotechnology, Chemical Engineering, or other relevant, closely related field.

+ Ability to conduct and communicate creative, independent research as proven through a record of technical accomplishment (e.g., publications in high-impact scientific journals, presentations at national/international conferences, etc.)

Qualifications We Desire:

+ Familiarity with AI-based tools for deep research and hypothesis generation and their application to the biosciences

+

Experience with Agentic AI

+ Proficiency in machine learning or statistical modeling applied to biological datasets (e.g., model-guided strain optimization, feature selection for phenotype prediction)

+ Strong background in metabolic engineering, systems biology, or computational biology

+ Experience in proteomic, transcriptomic and metabolomic data analysis

+

Experience with genome-scale metabolic models (GEMs), including model curation, constraint-based modeling, and flux balance analysis (FBA)

+ Familiarity with pathway and retro biosynthesis design tools

+ Knowledge of synthetic biology design principles and experience interfacing computational predictions with experimental implementation  

About Our Team:

The Biomanufacturing and Biomaterials department is dedicated to developing and advancing innovative technologies for producing the next generation of biofuels and biomaterials. Our multidisciplinary staff represents a diverse range of backgrounds and skill sets: molecular biology, chemistry, chemical and biochemical engineering, biophysics, computational science, and biochemistry. We partner broadly with other Sandia departments as well as other national…