Computational Modeling - Postdoctoral Research Staff Member

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.

Employment type
Full-time

Work arrangement
No