Postdoctoral Appointee - Atomistic Modeling​/Machine Learning, Hybrid

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Sandia National Laboratories is the nation’s premier science and engineering lab for national security and technology innovation, with teams of specialists focused on cutting-edge work in a broad array of areas. Some of the main reasons we love our jobs:

Challenging work with amazing impact that contributes to security, peace, and freedom worldwide

Extraordinary co-workers

Some of the best tools, equipment, and research facilities in the world

Career advancement and enrichment opportunities

Flexible work arrangements for many positions include 9/80 (work 80 hours every two weeks, with every other Friday off) and 4/10 (work 4 ten-hour days each week) compressed workweeks, part-time work, and telecommuting (a mix of onsite work and working from home)

Generous vacation, strong medical and other benefits, competitive 401k, learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance*

World-changing technologies. Life-changing careers. Learn more about Sandia at: http://www.sandia.gov

* These benefits vary by job classification.

What Your Job Will Be Like:

We are seeking a postdoctoral researcher with a background in density functional theory (DFT) and machine learning (ML) to join our multidisciplinary team of materials scientists. You bring passion for learning and energy for combining first-principles atomistic simulations with ML/AI to develop novel approaches accelerating multiscale simulation of reactive systems.

On any given day, you may be called on to:

Establish a high-throughput workflow that uses DFT to sample chemical reactions.

Develop an ML/AI framework that can predict chemical reaction events and integrate that framework with molecular dynamics codes (i.e., LAMMPS).

Plan, conduct, and analyze atomistic simulations to study catalysis in complex material systems.

Integrate materials knowledge to formulate research hypotheses, propose and carry out research.

Work in collaborative team environments on multidisciplinary, technically challenging projects as part of a dynamic research organization.

Communicate accomplishments in presentations at conferences and publications in peer-reviewed journals and engage with program managers to convey impact and research direction.

Due to the nature of the work, the selected candidate must be able to work hybrid.

Qualifications We Require:

PhD in Materials Science, Physics, Chemistry, Mechanical Engineering, or closely related field.

Ability to obtain and maintain a DOE Q-level security clearance.

Qualifications We Desire:

Demonstrated expertise with DFT simulations and codes (e.g., VASP, Quantum Espresso, Gaussian, DFTB+, etc.).

Experience developing and applying state-of-the-art ML/AI architectures and approaches to atomistic simulation methods (e.g., machine-learned interatomic potentials, graph neural networks, atomic cluster expansion descriptors, etc.).

Proficiency with programming languages (e.g., Python, C++) and ML packages (e.g., PyTorch, Tensor Flow).

Experience with atomistic simulations of catalysis, adsorption, and/or interfacial science (particularly involving polymers, metals, and/or MOFs).

Track record of collaborating with experimentalists and computational researchers to advance materials for engineering applications.

Strong written and verbal communication skills evidenced by a history of publishing original research in peer-reviewed journals and presentations at professional conferences.

Demonstrated ability to build effective working relationships and develop creative solutions in team environments.

About Our Team:

The Computational…