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Posted 15 hours agoComputational Scientist – Microbial Metabolic Modeling and Simulation
On-site
Responsibilities
- 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
- 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
- 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.
- 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
- 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.
Requirements
- Qualifications Education: Ph.D. in Bioengineering, Chemical Engineering, Computational Biology, Bioinformatics, Biochemistry, Systems Biology, or a related field.
- 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 in some, but not necessarily all, of the listed areas. 1) Kinetic & Dynamic Modeling: Knowledge of dynamic flux balance analysis (dFBA) or kinetic modeling of metabolic pathways 2) F amiliarity 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. 3) Au tomation & Scripting :
- Experience with high-throughput scripting, cloud computing, or automated pipeline workflows (Python, R, MATLAB) for scale-level simulation and data analysis. 4) F ermentation Knowledge: Familiarity with bioreactor operation modes (batch, fed-batch, continuous) and the biophysical parameters governing cell growth and product synthesis. 5) M ulti-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. 6) P rocess Scale-Up Support: Familiarity with commercial/industrial bioprocess applications and predicting metabolic shifts during scale up from laboratory- to pilot-scale bioreactors.
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Experience
- Candidates with an M.S. and 2+ years of experience will be considered.
Benefits
- Clearance Level No Clearance The salary range for this role is: $88,500 - $125,475 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.
- benefits package including medical, dental vision, 401K with company matching, a 9/80 work schedule and a paid holiday shutdown.
- We are proud to be an EEO/AA Equal Opportunity Employer, including disability/veterans.
Contact
- 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.
Additional details
- Worker Type Regular Job Description Computational Scientist – Microbial Metabolic Modeling and Simulation Location: Dayton, OH (AFRL / WPAFB) Employment Type: Full-Time Team: Biological Materials and Processing Research Team 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
- 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.
- 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.
- 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.
- 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.
- 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.
- experience modeling standard industrial hosts ( E. coli , yeast) and/or non-conventional microbial platforms. Citizenship: U.S.