Postdoctoral Research Staff Member - Machine Learning

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Postdoctoral Research Staff Member - Machine Learning

Science and Technology on a Mission!
For more than 60 years, the Lawrence Livermore National Laboratory (LLNL) has applied science and technology to make the world a safer place.

We have an opening for a Postdoctoral Research Staff Member in the area of machine learning modeling of corrosion on actinide metal and metal-oxide surfaces. You will actively participate in research involving the creation of neural network potentials for molecular simulation designed to identify and understand chemical reaction mechanisms of surface adsorbed atmospheric gases. This position is in the Reaction Dynamics Group of the Materials Science Division.

Essential Duties
- Conduct research in quantum calculations and neural network based model development of high-Z materials and their surface chemistry.
- Contribute to the conception, design, and execution of research related to the study of materials under extreme thermodynamic conditions.
- Contribute to creating a multi-scale simulation approach that leverages a combination of quantum calculations, molecular dynamics, and coarse-grained simulations.
- Utilize novel computational models to help interpret long timescale reaction dynamics observed in experiments on identified systems.
- Pursue independent but complementary research interests and interact with a broad spectrum of scientists internally and externally to the Laboratory.
- Collaborate with scientists inamultidisciplinary team environment to accomplish research goals.
- Maintain and establish laboratory protocols.
- Document research; publish papers in peer-reviewed journals, and present results within the DOE community and at conferences.
- Perform other duties as assigned.

- Recent PhD in Physics, Chemistry, Engineering or related field.
- Experience in first-principles simulation techniques of condensed phases and molecular dynamics simulation approaches.
- Experience with programming in C, FORTRAN, or an equivalent high-level language.
- Ability to develop independent research projects through publication of peer-reviewed literature.
- Proficient verbal and written communication skills as reflected in effective presentations at seminars, meetings and/or teaching lectures.
- Initiative and interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment.

Pre-Employment Drug Test: External applicant(s) selected for this position will be required to pass a post-offer, pre-employment drug test.

Anticipated Clearance Level: Q (Position will be cleared to this level). Applicants selected will be subject to a Federal background investigation and must meet eligibility requirements for access to classified information or matter. In addition, all L or Q cleared employees are subject to random drug testing. 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.

Note: Text revised effective April 20, 2017.

This is a two-year Postdoctoral appointment with the possibility of extension to a maximum of three years. Eligible candidates are recent PhDs within five years of the month of the degree award at time of hire date.

To apply, visit

About Us
Lawrence Livermore National Laboratory (LLNL), located in the San Francisco Bay Area (East Bay), is a premier applied science laboratory that is part of the National Nuclear Security Administration (NNSA) within the Department of Energy (DOE). LLNL's mission is strengthening national security by developing and applying cutting-edge science, technology, and engineering that respond with vision, quality, integrity, and technical excellence to scientific issues of national importance. The Laboratory has a current annual budget of about $1.5 billion, employing approximately 6,000 employees.

LLNL is an affirmative action/ equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, protected veteran status, age, citizenship, or any other characteristic protected by law.

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