Computational Modeling - Postdoctoral Research Staff Member

Computational Modeling - Postdoctoral Research Staff Member Full-time Organization: Physical and Life Sciences Category: Physical Life Sciences Employee Referral Bonus: Not applicable Job Code 1: PDS.1 Post-Dr Research Staff 1 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: Post Doctoral 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. We have an opening for a Postdoctoral Researcher in the areas of atomistic and multiscale modeling to optimize metal joining in alloyed systems. The overarching goal of this project is to develop an integrated simulation-experiment workflow to maximize the utility of lab-scale tests by elucidating the fundamental scientific principles governing metal part joining. You will actively participate in the development of Machine-learning interatomic potentials (MLIP) for large-scale atomistic modeling with quantum-mechanical accuracy for use in Grand Canonical grain boundary interface searches and the determination of subsequent interfacial properties. This work will occur in conjunction with mesoscale modeling and experimental teams to determine the effects of interfacial defects, impurities, phase transformations, and transient liquid phases on 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 (EMC). 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 Perform and analyze first principles-based simulations (e.g., DFT, DFTB, etc.) to generate reference and validation data. Develop GPU-enabled MLIP models for complex intermetallic and alloyed systems for use in grain boundary and interface modeling. Provide inputs for mesoscale and thermodynamic modeling approaches, including mass transport properties and interfacial energies. Development and maintenance of our advanced atomistic simulation codes for interatomic model optimization and simulation. Aid in maintaining an organized and accessible materials data base 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 a broad spectrum of scientists internally and externally to the Laboratory. Perform other duties as assigned. Ability to secure and maintain a U.S. DOE Q-level security clearance, which requires U.S. Citizenship. PhD in Materials Science, Condensed Matter Physics, Chemistry, Chemical Engineering, or a related field. Experience with scientific code development, including programming in python, C++, C, Fortran, and/or an equivalent level 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 for problems in chemistry or materials science. 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 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 statistics-based simulations including (kinetic) Monte Carlo and enhanced sampling methods. Experience with computational workflow development and multiscale modeling integration. Experience with simulations of structurally/chemically disordered grain boundaries and heterogeneous interfaces. Experience with AI/ML methods including surrogate modeling, feature engineering, generative models, Bayesian optimization, uncertainty quantification, sensitivity analysis, and Pareto front analysis. Pay Range $123,048 Annually This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs. #LI-Hybrid Position Information This is a Postdoctoral appointment with the possibility of extension to a maximum of three years. The postdoctoral appointment is a temporary, mentored training position intended for individuals in the early stages of their postdoctoral research careers. Consistent with the structure of the program, applicants must have received their Ph.D. (or equivalent doctorate) within five years of the anticipated start date. Why Lawrence Livermore National Laboratory? Included in 2026Best Places to Work by Glassdoor! Flexible schedules (*depending on project needs) This position requires a Department of Energy (DOE) Q-level clearance.If you are selected, wewill initiate a Federal background investigation to determine if youmeet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship. Pre-Employment Drug Test External applicant(s) selected for this position must pass a post-offer, pre‑employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor. 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