Computational Scientist – Microbial Metabolic Modeling and Simulation

Worker Type
:
Regular

AV is seeking a Computational Scientist specializing in Microbial Metabolic Modeling and Simulation to 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. You will develop predictive genome‑scale metabolic models (GEMs), simulate metabolic fluxes, and identify genetic intervention strategies to maximize yield, titer, and productivity of target molecules.

Develop computational models of microbial systems and write code to analyze and predict microbial behavior. Collaborate closely with laboratory scientists to design experiments, interpret results, and refine models based on experimental data. Prior wet‑lab experience is highly desirable, as this role requires serving as an active research partner rather than solely a computational contributor. You will work alongside scientists responsible for conducting laboratory experiments and will help integrate computational and experimental approaches to advance research objectives.

Maintain a strong feedback loop between laboratory scientists and the modeling team, using experimental results to iteratively refine computational models and guide subsequent rounds of laboratory research, improving accuracy and accelerating discovery.

• 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.

• 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.

• 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…