Computational Scientist – Microbial Metabolic Modeling and Simulation

Worker Type

Job Description

 

Position Overview

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.

Key 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. You will 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.

Required Qualifications

• 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, to include:

• 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

Candidates may not possess all of these qualifications; it is expected that they will have experience in some, but not necessarily all, of the listed areas.

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

Candidates may not possess all of these qualifications; it is expected that they will have experience in some, but not necessarily all, of the listed areas.

• 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. 

Clearance Level

 

The salary range for this role is:

AeroVironment considers several factors when extending an offer, including but not limited to, the location, the role and associated responsibilities, a candidate’s work experience, education/training, and key skills.

ITAR Requirement:

This position requires access to information that is subject to compliance with the International Traffic Arms Regulations (“ITAR”) and/or the Export Administration Regulations (“EAR”). In order to comply with the requirements of the ITAR and/or the EAR, applicants must qualify as a U.S. person under the ITAR and the EAR, or a person to be approved for an export license by the governing agency whose technology comes under its jurisdiction. Please understand that any job offer that requires approval of an export license will be conditional on AeroVironment’s determination that it will be able to obtain an export license in a time frame consistent with AeroVironment’s business requirements. A “U.S. person” according to the ITAR definition is a U.S. citizen, U.S. lawful permanent resident (green card holder), or protected individual such as a refugee or asylee. See 22 CFR § 120.15. Some positions will require current U.S. Citizenship due to contract requirements.

Benefits:  AV offers an excellent benefits package including medical, dental vision, 401K with company matching, a 9/80 work schedule and a paid holiday shutdown.  For more information about our company benefit offerings please visit:  http://www.avinc.com/myavbenefits.

We also encourage you to review our company website at http://www.avinc.com to learn more about us.

Principals only need apply.  NO agencies please.

About AV:

AV isn’t for everyone. We hire the curious, the relentless, the mission-obsessed. The best of the best.

We don’t just build defense technology—we redefine what’s possible. As the premier autonomous systems company in the U.S., AV delivers breakthrough capabilities across air, land, sea, space, and cyber. From AI-powered drones and loitering munitions to integrated autonomy and space resilience, our technologies shape the future of warfare and protect those who serve.

Founded by legendary innovator Dr. Paul MacCready, AV has spent over 50 years pushing the boundaries of what unmanned systems can do. Our heritage includes seven platforms in the Smithsonian—but we’re not building history, we’re building what’s next.

If you're ready to build technology that matters—with speed, scale, and purpose—there’s no better place to do it than AV.

We are proud to be an EEO/AA Equal Opportunity Employer, including disability/veterans. AeroVironment, Inc. is an Equal Employment Opportunity (EEO) employer and welcomes all qualified applicants. Qualified applicants will receive fair and impartial consideration without regard to race, sex, color, religion, national origin, age, disability, protected veteran status, genetic data, sexual orientation, gender identity or other legally protected status.

ITAR