Computational Modeling - Postdoctoral Research Staff Member

LLNL • Livermore, CA

13d • Hybrid

About The Position

Lawrence Livermore National Laboratory is hiring 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 maximizes the utility of lab-scale tests and elucidates the scientific principles governing metal-part joining. The researcher will develop machine-learning interatomic potentials (MLIPs) for large-scale, quantum-mechanical-accuracy atomistic modeling, including Grand Canonical grain-boundary interface searches and analysis of interfacial properties. The work is conducted alongside 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, within the Physical & Life Sciences Directorate, supporting the Energetic Materials Center.

Depending on the assignment, the position 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, potentially extendable up 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 role requires eligibility for a U.S. Department of Energy Q-level security clearance, which requires U.S. citizenship.

Requirements

• U.S. citizenship and ability to secure and maintain a U.S. DOE Q-level security clearance.
• Ph.D. in Materials Science, Condensed Matter Physics, Chemistry, Chemical Engineering, or a related field.
• Doctorate received within five years of the anticipated start date.
• Experience with scientific code development, including Python, C++, C, Fortran, or an equivalent compiled language.
• Experience with electronic-structure calculations using 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 desire and ability to work in a collaborative, multidisciplinary team environment.

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 machine-learning methods, including surrogate modeling, feature engineering, generative models, Bayesian optimization, uncertainty quantification, sensitivity analysis, and Pareto-front analysis.

Responsibilities

• Perform and analyze first-principles 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 peer-reviewed papers, 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

• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (depending on project needs)

What This Job Offers

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