Onsite Postdoctoral Appointee — Materials ML & Informatics

Sandia National Laboratories; Livermore (CA); On-site; USD 90,000 - 120,000; Full time

Benefits offered by this job

Relocation assistance

Medical benefits

401k

Flexible work arrangements

Job summary

Sandia National Laboratories in Livermore, CA, seeks a postdoctoral appointee to develop data-driven computational methods for materials informatics, bridging experiments and modeling. You will build interpretable ML models that relate chemistry, morphology and processing to performance, guiding experiments and enabling rapid discovery across multiple materials systems.

The role emphasizes collaboration with chemists and materials scientists, active learning, and dissemination of results through

Qualifications

• PhD in materials science and engineering, chemistry, chemical engineering, physics, applied mathematics, computer science, data science, or a related field.
• Demonstrated experience building, evaluating and applying ML-based models to experimental and/or computational datasets.
• Familiarity with ML techniques for structure-property relationships in distinct materials systems.
• Experience with probabilistic surrogate models or active learning for scientific discovery.
• Ability to obtain and maintain DOE Q clearance.
• Strong record of technical accomplishments and written communication.

Responsibilities

• Develop and apply computational, statistical, and ML approaches to analyze heterogeneous datasets to establish structure-property relationships in materials.
• Build predictive and interpretable models connecting material chemistry, morphology, processing history, and environmental exposure to outcomes.
• Collaborate with experimental scientists to integrate modeling with characterization data and guide interpretations.
• Contribute to design of next-step experiments or screening strategies using uncertainty-aware modeling or active learning.
• Communicate progress and findings through reports and publications.

Skills

Machine learning

Data analysis

Python

Computational modeling

Materials informatics

Active learning

Education

PhD in materials science/engineering or related

Tools

TensorFlow

scikit-learn

PyTorch

Job description

Sandia National Laboratories in Livermore, CA, seeks a postdoctoral appointee to develop data-driven computational methods for materials informatics, bridging experiments and modeling. You will build interpretable ML models that relate chemistry, morphology and processing to performance, guiding experiments and enabling rapid discovery across multiple materials systems.

The role emphasizes collaboration with chemists and materials scientists, active learning, and dissemination of results through