Computational Scientist – Microbial Metabolic Modeling and Simulation

About the role

The Computational Scientist will work with the AFRL Biological Materials and Processing Research Team to spearhead the in silico design, evaluation, and optimization of microbial hosts for advanced bioproduction and material synthesis.

Responsibilities

• Translate In Silico Designs to the Bench: Serve as the primary bridge between dry-lab and wet-lab operations; take candidate metabolic pathways, knock-out strategies, and over-expression targets generated via computational modeling and successfully translate them into actionable engineering strategies for the wet-lab team.
• Metabolic Network Reconstruction & Simulation: Generate, curate, and refine genome-scale metabolic models (GEMs) using advanced systems biology and constraint-based modeling techniques.
• High-Throughput Simulation & Selection: Develop and execute robust, automated high-throughput computational workflows (such as Flux Balance Analysis [FBA], MOMA, or regulatory flux modeling) to screen thousands of genetic perturbation strategies, successfully isolating rare "hit" strain designs from background metabolic noise.
• Data Integration & Loop Closure: Analyze multi-omics and fermentation data (transcriptomics, metabolomics, fluxomics) to identify sequence-activity and flux-yield relationships. Feed this high-quality experimental data back into the computational models to validate predictive capabilities, troubleshoot failures, and guide the design of the next, smarter round of strain optimization.
• Model & Process Optimization: Continually refine modeling constraints (pH, maintenance energy, substrate uptake rates, toxicity parameters) to ensure the simulation environment accurately reflects industrial bioprocess and fermentation conditions. Test predictive metabolic models against candidate strain performance at pilot-scale levels.

Requirements

• Education: Ph.D. in Bioengineering, Chemical Engineering, Computational Biology, Bioinformatics, Biochemistry, Systems Biology, or a related field. Candidates with an M.S. and 2+ years of experience will be considered.
• Citizenship: U.S. Citizenship is required due to government facility access requirements.
• Research Exposure With Metabolic Engineering & Modeling: Designed, optimized, and characterized microbial metabolic networks using state-of-the-art computational biology, constraint-based modeling, and systems-level approaches to predict and improve metabolic flux.
• Collaborated closely with experimental scientists to translate computational models into engineered strains and scalable biological solutions for industrial and real-world applications, maintaining a strong feedback loop between in silico predictions and laboratory validation.
• Applied metabolic engineering principles to microbial hosts including Corynebacterium, Escherichia coli, and/or Saccharomyces cerevisiae to guide strain design and pathway optimization.
• Contributed to peer-reviewed publications and incorporated this research as a significant component of a Ph.D. dissertation, demonstrating expertise in integrating computational modeling with experimental metabolic engineering.
• Technical Expertise: Proficiency in constraint-based metabolic modeling (e.g., COBRA toolbox in Python/MATLAB) and experience modeling standard industrial hosts (E. coli, yeast) and/or non-conventional microbial platforms.

Preferred Skills

• Kinetic & Dynamic Modeling: Knowledge of dynamic flux balance analysis (dFBA) or kinetic modeling of metabolic pathways.
• Familiarity with Wet-Lab Strain Construction: Understanding of advanced molecular biology techniques for strain engineering (e.g., CRISPR/Cas9, multiplex automated genome engineering, Gibson Assembly) to optimize collaboration with wet-lab peers.
• Automation & Scripting: Experience with high-throughput scripting, cloud computing, or automated pipeline workflows (Python, R, MATLAB) for scale-level simulation and data analysis.
• Fermentation Knowledge: Familiarity with bioreactor operation modes (batch, fed-batch, continuous) and the biophysical parameters governing cell growth and product synthesis.
• Multi-Omics Integration: Experience with (or willingness to learn) integration datasets (transcriptomics, proteomics, metabolomics) into metabolic flux models to help interpret experimental data and refine constraints.
• Process Scale-Up Support: Familiarity with commercial/industrial bioprocess applications and predicting metabolic shifts during scale up from laboratory- to pilot-scale bioreactors.

Benefits

• Medical, dental, vision, 401K with company matching, a 9/80 work schedule, and a paid holiday shutdown.