Computational Modeling - Postdoctoral Research Staff Member

LLNL • Livermore, CA

12d • Hybrid

About The Position

Lawrence Livermore National Laboratory is seeking a Postdoctoral Researcher in atomistic and multiscale modeling to optimize metal joining in alloyed systems. The project aims to develop an integrated simulation-experiment workflow that makes lab-scale tests more useful by identifying the fundamental scientific principles governing metal-part joining. The researcher will develop machine-learning interatomic potentials (MLIPs) for large-scale atomistic modeling with quantum-mechanical accuracy, including use in Grand Canonical grain-boundary interface searches and determination of interfacial properties. Work will be coordinated with mesoscale modeling and experimental teams to study how interfacial defects, impurities, phase transformations, and transient liquid phases affect joining strength.

The position is in the Materials Dynamics and Kinetics group of the Materials Science Division in the Physical & Life Sciences Directorate, supporting the Energetic Materials Center. Depending on assignment, the role may offer a hybrid schedule with in-person and virtual work and the possibility of working from home one or more days per week.

This is a temporary, mentored postdoctoral appointment that may be extended to a maximum of three years. Applicants must have received their Ph.D. or equivalent doctorate within five years of the anticipated start date. The position requires eligibility for a U.S. DOE Q-level security clearance, which requires U.S. citizenship.

Requirements

• Ph.D. in Materials Science, Condensed Matter Physics, Chemistry, Chemical Engineering, or a related field.
• Ability to secure and maintain a U.S. DOE Q-level security clearance; U.S. citizenship is required for the clearance.
• Experience with scientific code development, including programming in Python, C++, C, Fortran, or an equivalent compiled language.
• Experience with electronic-structure calculations using packages such as VASP, Quantum Espresso, or similar codes.
• Experience with molecular-dynamics model development and applying atomistic machine-learning methods to chemistry or materials-science problems.
• Experience with large-scale atomistic simulations in high-performance computing environments.
• Ability to develop independent research projects through publication of peer-reviewed literature.
• Proficient verbal and written communication skills, demonstrated through effective presentations at seminars, meetings, and/or teaching lectures.
• Self-motivation, excellent interpersonal skills, and the ability and desire to work in a collaborative, multidisciplinary team environment.
• Must have received a Ph.D. or equivalent doctorate within five years of the anticipated start date.

Nice To Haves

• Experience with statistics-based simulations, including (kinetic) Monte Carlo and enhanced-sampling methods.
• Experience with computational-workflow development and multiscale-modeling integration.
• Experience simulating structurally or chemically disordered grain boundaries and heterogeneous interfaces.
• Experience with SI (Super Intelligence, formerly referred to as Artificial Intelligence, or AI) and/or ML methods, including surrogate modeling, feature engineering, generative models, Bayesian optimization, uncertainty quantification, sensitivity analysis, and Pareto-front analysis.

Responsibilities

• Perform and analyze first-principles-based simulations, such as DFT and DFTB, to generate reference and validation data.
• Develop GPU-enabled machine-learning interatomic-potential models for complex intermetallic and alloyed systems for grain-boundary and interface modeling.
• Provide inputs for mesoscale and thermodynamic modeling, including mass-transport properties and interfacial energies.
• Develop and maintain advanced atomistic simulation codes for interatomic-model optimization and simulation.
• Help maintain an organized and accessible materials database for ML-driven experimental predictions.
• Document research; write and publish papers in peer-reviewed journals; and present results within the DOE community, at working group meetings, and at conferences.
• Pursue independent but complementary research interests and interact with scientists within and outside the Laboratory.
• Perform other duties as assigned.

Benefits

• 401(k)
• Relocation assistance
• Education reimbursement program
• Flexible schedules, depending on project needs
• Flexible benefits package

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree