Postdoc: Computational Materials ML for Predictive Design

Sandia National Laboratories; Livermore (CA); On-site; USD 70,000 - 95,000; Full time

Benefits offered by this job

Flexible work arrangements

Relocation assistance

Generous vacation

Job summary

Sandia National Laboratories seeks a postdoctoral appointee with expertise in computational materials chemistry, materials informatics, and predictive modeling to join an interdisciplinary team tackling materials challenges. You will connect experimental observations with data-driven models across multiple Sandia sites.

You will work on structure-property relationships, uncertainty-aware modeling, and collaboration with chemists and materials scientists to accelerate discovery and application of

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 machine learning based models to datasets.
• Familiarity with machine learning techniques for structure-property relationships in 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 machine-learning approaches to analyze heterogeneous experimental and simulation datasets to establish structure-property-performance relationships in materials.
• Build predictive and interpretable models linking material chemistry, morphology, processing history, and environmental exposure to performance outcomes.
• Collaborate with experimental materials scientists and chemists to integrate modeling with characterization data.
• Contribute to design of next-step experiments or screening strategies using uncertainty-aware modeling or active learning.
• Collaborate across multidisciplinary teams including researchers from Sandia and universities.
• Communicate progress and findings through reports, publications, and presentations.

Skills

ML modeling

Data analysis

Python

Materials informatics

Education

PhD in materials science/chemistry/physics or related

Tools

Python

scikit-learn

TensorFlow

Job description

Sandia National Laboratories seeks a postdoctoral appointee with expertise in computational materials chemistry, materials informatics, and predictive modeling to join an interdisciplinary team tackling materials challenges. You will connect experimental observations with data-driven models across multiple Sandia sites.

You will work on structure-property relationships, uncertainty-aware modeling, and collaboration with chemists and materials scientists to accelerate discovery and application of