Postdoctoral Appointee: Hydrogen–Metal Materials Design & ML

Sandia National Laboratories; California (MO); On-site; USD 70,000 - 90,000; Full time

Benefits offered by this job

Flexible work arrangements

Relocation assistance

Generous benefits

Job summary

Sandia National Laboratories is seeking a highly motivated Postdoctoral Appointee to advance computational discovery and design of materials with tunable hydrogen–metal interactions. You will integrate first-principles simulations, thermodynamic modeling, and machine learning to predict diffusion, absorption, and phase stability in AB2-type alloys, metallic glasses, and related systems.

The role requires collaboration with experimental teams, automated experimentation laboratories, and industry

Qualifications

• PhD in materials science, chemical engineering, mechanical engineering, physics, chemistry, applied mathematics, computer science, or closely related field.
• Ability to obtain and maintain a DOE Q clearance.

Responsibilities

• Perform first-principles calculations (DFT) to study hydrogen–metal interactions.
• Develop computational models for hydrogen absorption, diffusion, and transport in metallic systems.
• Apply thermodynamic modeling and statistical mechanics to connect atomistic data with observables.
• Build ML-based materials property predictors and uncertainty-aware screenings.
• Develop Python-based workflows for data processing, simulation automation, model training, and analysis.
• Collaborate with experimental teams and industry partners to guide synthesis and testing.

Skills

Computational materials science

Density functional theory

Python programming

Thermodynamics

Statistical mechanics

Machine learning

Experimental collaboration

High-throughput workflows

Education

PhD in materials science / closely related field

Tools

VASP

Quantum ESPRESSO

GPAW

LAMMPS

ASE / pymatgen

FireWorks / atomate

scikit-learn / PyTorch / JAX

Job description

Sandia National Laboratories is seeking a highly motivated Postdoctoral Appointee to advance computational discovery and design of materials with tunable hydrogen–metal interactions. You will integrate first-principles simulations, thermodynamic modeling, and machine learning to predict diffusion, absorption, and phase stability in AB2-type alloys, metallic glasses, and related systems.

The role requires collaboration with experimental teams, automated experimentation laboratories, and industry