"Postdoctoral Research Associate - Engineering"

Position Summary

We are seeking a qualified Postdoctoral Research Associate to join our team, working on both NSF ERC and DOE cyanobacterial bio grants. The successful candidate will focus on sustainable waste treatments and bio-productions. Key responsibilities include conducting fermentation, bioreactor operations, and cell growth analysis using imaging, OD analysis, and HPLC metabolite measurement. Additional duties involve microbial population analysis (16sRNA), chemistry analysis (N, P, metals), and statistical modeling.

The role also entails lab management, including cleaning and safety monitoring, student mentoring, proposal and paper writing, and guest teaching for a bioprocess engineering class. The candidate will be responsible for project coordination and reporting.

Job Description

Information on being a postdoc at WashU in St. Louis can be found at https://postdoc.wustl.edu/prospective-postdocs-2/.

Trains under the supervision of a faculty mentor including (but not limited to):

• Work under both NSF ERC center and DOE cyanobacterial bio grant, focusing on sustainable waste treatments and bio-productions.
• Fermentation, bioreactor operations and cell growth analysis (imaging and OD analysis, HPLC metabolite measurement).
• Microbial population analysis (16sRNA). Chemistry analysis (N, P, metals).
• Statistical modeling.
• Use of large language model for knowledge synthesis and RAG.
• Lab management, cleaning and safety monitoring, and student mentoring.
• Proposal and paper writing.
• Guest teaching for bioprocess engineering class.
• Project coordination/reporting.

Salary Range: Base pay is commensurate with experience.

Required Qualifications

Education: Ph.D., M.D. Or Equivalent Terminal Or Doctoral Degree.

Certifications: No specific certification is required for this position.

Work Experience: No specific work experience is required for this position.

Skills: Not Applicable

Driver's License: A driver's license is not required for this position.

Experience with large language models for knowledge synthesis and Retrieval-Augmented Generation (RAG) is essential.