Computational Modeling - Postdoctoral Research Staff Member

Lawrence Livermore National Laboratory
Livermore, CA 94550

We have an opening for a Postdoctoral Researcher in the area of atomistic simulations and machine learning for materials aging and compatibility. You will use atomistic simulations to probe origins of high strain rate response in polymer materials, and also apply machine learning on diverse material characterization data to predict relevant performance metrics and how they change with material aging. The selected candidate will collaborate with a multidisciplinary team comprised of computer scientists, materials scientists, engineers, and chemists. This position is in the Reaction Dynamics group of the Materials Science Division.

Note: This is a two-year Postdoctoral appointment with the possibility of extension to a maximum of three years.

In this role you will

  • Perform and analyze atomistic simulations to probe microscopic origins of high strain rate response in polymeric materials
  • Collaborate with magnetohydrodynamics modelers to develop improved materials strength models from resulting data
  • Leverage machine learning and statistical techniques (ML) to determine relationships between material properties, performance, and aging
  • Actively pursue new ways of applying ML to chemistry and materials science to meet sponsor needs in appropriate national security areas
  • Contribute to the conception, design, and execution of research related to the study of materials aging, compatibility, and performance
  • Document research; write and publish papers in peer-reviewed journals, and present results within the DOE community, at working group meetings, and at conferences
  • Perform other duties as assigned.

  • Ability to secure and maintain a U.S. DOE Q-level security clearance with requires U.S. citizenship
  • PhD in Chemistry, Physics, Chemical Engineering, Materials Science, or a related field
  • Experience running and analyzing molecular dynamics or Monte Carlo simulations of conformationally complex materials (e.g., polymers)
  • Experience with large-scale simulations in high-performance computing environments using software packages such as LAMMPS, GROMACS, NAMD or similar codes.
  • Proficient with Python or similar scripting language
  • Proficient verbal and written communication skills as reflected in effective presentations at seminars, meetings and/or teaching lectures
  • Self-motivated and excellent interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment

Qualifications We Desire

  • Experience with non-equilibrium simulations
  • Experience with electronic structure calculations using packages such as Gaussian, NWChem, VASP, or similar codes. Experience applying machine learning methods for problems in chemistry or materials science
  • Ability to develop independent research projects through publication of peer-reviewed literature.

Why Lawrence Livermore National Laboratory?

  • Included in 2020 Best Places to Work by Glassdoor!
  • Work for a premier innovative national Laboratory
  • Comprehensive Benefits Package
  • Flexible schedules (*depending on project needs)
  • Collaborative, creative, inclusive, and fun team environment

Learn more about our company, selection process, position types and security clearances by visiting our Career site.

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LLNL demonstrates its commitment to public safety by requiring that all new Laboratory employees be immunized against COVID-19 unless granted an accommodation under applicable state or federal law. This requirement will apply to all new hires including those who will be working on site, as well as those who will be teleworking.

Security Clearance
If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. In addition, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship. If you hold multiple citizenships (U.S. and another country), you may be required to renounce your non-U.S. citizenship before a DOE L or Q clearance will be processed/granted. For additional information, please see DOE Order 472.2.

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If you need assistance and/or a reasonable accommodation during the application or the recruiting process, please submit a request via our online form.

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Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to strengthen the United States' security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are looking for individuals that demonstrate an understanding of working in partnership with team peers, who engage, advocate, and contribute to building an inclusive culture, and provide expertise to solve challenging problems.

"},"jobDescription":{"title":"Job Description","text":"

We have an opening for a Postdoctoral Researcher in the area of atomistic simulations and machine learning for materials aging and compatibility. You will use atomistic simulations to probe origins of high strain rate response in polymer materials, and also apply machine learning on diverse material characterization data to predict relevant performance metrics and how they change with material aging. The selected candidate will collaborate with a multidisciplinary team comprised of computer scientists, materials scientists, engineers, and chemists. This position is in the Reaction Dynamics group of the Materials Science Division.

Note: This is a two-year Postdoctoral appointment with the possibility of extension to a maximum of three years.

In this role you will

  • Perform and analyze atomistic simulations to probe microscopic origins of high strain rate response in polymeric materials
  • Collaborate with magnetohydrodynamics modelers to develop improved materials strength models from resulting data
  • Leverage machine learning and statistical techniques (ML) to determine relationships between material properties, performance, and aging
  • Actively pursue new ways of applying ML to chemistry and materials science to meet sponsor needs in appropriate national security areas
  • Contribute to the conception, design, and execution of research related to the study of materials aging, compatibility, and performance
  • Document research; write and publish papers in peer-reviewed journals, and present results within the DOE community, at working group meetings, and at conferences
  • Perform other duties as assigned.
"},"qualifications":{"title":"Qualifications","text":"
  • Ability to secure and maintain a U.S. DOE Q-level security clearance with requires U.S. citizenship
  • PhD in Chemistry, Physics, Chemical Engineering, Materials Science, or a related field
  • Experience running and analyzing molecular dynamics or Monte Carlo simulations of conformationally complex materials (e.g., polymers)
  • Experience with large-scale simulations in high-performance computing environments using software packages such as LAMMPS, GROMACS, NAMD or similar codes.
  • Proficient with Python or similar scripting language
  • Proficient verbal and written communication skills as reflected in effective presentations at seminars, meetings and/or teaching lectures
  • Self-motivated and excellent interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment

Qualifications We Desire

  • Experience with non-equilibrium simulations
  • Experience with electronic structure calculations using packages such as Gaussian, NWChem, VASP, or similar codes. Experience applying machine learning methods for problems in chemistry or materials science
  • Ability to develop independent research projects through publication of peer-reviewed literature.

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Computational Modeling - Postdoctoral Research Staff Member

Lawrence Livermore National Laboratory
Livermore, CA 94550

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