Computational Chemistry and Data Analytics - Academic Graduate Appointee

• Full-time
• Organization:
  Physical and Life Sciences
• Category:
  Physical Life Sciences
• Employee Referral Bonus:
  Not applicable
• Job Code 1: 710.0 Academic Graduate Appointee Ex
• Pre-Employment Drug Test:
  Required for external applicant(s) selected for this position (includes testing for use of marijuana)
• Pre-Placement Medical Exam:
  Not applicable
• Position Type:
  Academic Graduate Appointee
• Security Clearance:
  Anticipated DOE Q clearance (requires U.S. citizenship and a federal background investigation)

Join us and make YOUR mark on the World!

Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.

Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.

Note: This is a one-year Academic Graduate Appointee with the possibility of extension to a maximum of two years.

We're looking for an Academic Graduate Appointee to support interdisciplinary research in computational chemistry, molecular design, data wrangling, machine learning, and high-performance data analysis by applying established computational methods to defined scientific data-analysis assignments under guidance. The role will assist with developing and maintaining reliable, reproducible workflows for managing and analyzing large-scale chemical, molecular, and biological datasets. Responsibilities include supporting the integration of simulation, experimental, and database data;

generating molecular descriptors; contributing to automated ETL pipelines; and creating tools to identify meaningful patterns. The successful candidate will collaborate across computational, experimental, and engineering disciplines to support molecular dynamics, small-molecule inhibitor discovery, AI-enabled drug discovery, and reproducible research methods. Assignments will provide opportunities to develop technical skills and professional experience under the guidance of experienced staff. This position is in the Biochemical and Biophysical Systems Group in the Biosciences and Biotechnology Division within the Physical and Life Sciences Directorate.

Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week.

You will

• Collaborate with computational chemists, computational biologists, experimental scientists, computer scientists, and engineers to support defined computational and data analytics assignments under guidance.
• Assist in implementing and maintaining data-wrangling workflows to collect, ingest, clean, transform, standardize, and validate chemical, molecular, simulation, and biological datasets using established methods.
• Contribute to the development and testing of ETL pipelines for high-volume scientific data, including molecular structures, simulation outputs, and molecular descriptors.
• Assist with data from computational databases, molecular dynamics simulations, high-throughput experiments, and external sources.
• Identify data-quality issues, including missing, duplicated, inconsistent, incomplete, or anomalous data using established data-quality practices and elevate nonroutine issues to senior team members.
• Develop basic data visualizations and analysis toolsto identify chemical, structural, and biological trends while maintaining well-documented,…