Postdoctoral Appointee, Computational Methods, Particulate Materials - Onsite
About Sandia:
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What Your Job Will Be Like:We are seeking a highly motivated Postdoctoral Appointee to join our dynamic research team focused on advancing the field of coarse‑grained particle‑based simulations for complex granular flows and powder compaction. This position offers a unique opportunity to engage in cutting‑edge research that bridges theoretical modeling and practical applications in high‑performance computing.
On any give day, you may be asked to:- Develop machine‑learned coarse‑grained mechanical inter‑particle potentials to improve simulation accuracy and efficiency.
- Investigate complex granular physics through particle‑based methods such as discrete element method (DEM), bonded particle models, and peridynamics, contributing to a deeper understanding of granular behavior.
- Run large‑scale simulations of granular and deformable particle systems using high‑performance computing resources, optimizing algorithms, and managing extensive datasets.
- Conduct research to validate models against experimental data and publishing findings in high‑impact journals.
Travel as needed to present research results at technical conferences, fostering professional connections and staying updated on advancements in the field. Due to the nature of the role, this candidate muse be able to work onsite.
Qualifications We Require:- PhD, conferred within 5 years prior to employment, in Engineering, Physics, Computer Science, Material Science, Applied Math, or other related field
- Theoretical expertise in the physics and mechanics of heterogeneous materials (e.g. granular materials, glasses)
- Demonstrated proficiency in particle‑based simulations (e.g. molecular dynamics, discrete element method, bonded particle models, meshless methods, etc.)
- Demonstrated scientific programming skills
- Good communication skills as evidenced by a history of publication of results in peer‑reviewed journals and external presentations at appropriate scientific conferences
- Ability to obtain and maintain a DoE Q security clearance
- Familiarity with high performance computing environments and parallel simulations and Linux operating systems and computing environments
- Experience using molecular dynamics or other particle‑based codes, e.g. LAMMPS
- Experience developing coarse‑grained and/or machine‑learned contact/potential models
- Experience developing and using machine learning
- Demonstrated ability to work in large, multi‑disciplinary teams
- Demonstrated ability to solve complex multiscale problems in physics
- Experience collaborating in multi‑organizational environments
We conduct applied research on the structure and behavior of complex engineered material systems exhibiting coupled physical processes that span multiple length and time scales. This research encompasses theoretical/computational and experimental methods and analysis of energetic (propellants, explosives, pyrotechnics) and soft materials (complex fluids, aerosols, polymers, powders and particles) and multiphase/composite systems. We support partner organizations in scientific assessments for…
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