Postdoctoral Appointee - AI​/ML Solid Mechanics, Onsite

This postdoctoral position is a temporary position for up to one year, which may be renewed at Sandia's discretion up to five additional years. The PhD must have been conferred within five years prior to employment. Individuals in postdoctoral positions may bid on regular Sandia positions as internal candidates, and in some cases may be converted to regular career positions during their term if warranted by ongoing operational needs, continuing availability of funds, and satisfactory job performance.

Employees, excluding student interns, must remain in their current position for one year from their hire date before bidding on a new job. However, the hiring manager may obtain approval to consider a candidate before the one-year requirement is met via the review process. This approval must be secured prior to being interviewed for a posted position. The application of this requirement will not supersede collective bargaining agreements.

Please refer to the collective bargaining labor agreements for eligibility requirements for represented bidders.

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* These benefits vary by job classification.

The Materials and Failure Modeling Department is seeking a highly motivated Postdoctoral Appointee to join our team and contribute to ongoing computational solid mechanics research, development, and analysis in the areas of scientific machine learning.

On any given day, you will have the opportunity to:

• Apply the principles of solid mechanics and advanced mathematics to support the research and
• Utilize advanced optimization methods, such as genetic algorithms, for discovering new material laws.
• Use state-of-the-art finite element codes and world class computational resources to support the
• Build and test agentic material modeling pipelines via pretrained large language models (LLMs).
• Develop a robust research portfolio through peer-reviewed journal publications, conference presentations, and participation in professional societies.

Applicants are requested to submit complete Curricula Vitae during the application process.

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

• PhD in Mechanical Engineering, Applied Mechanics, Aerospace Engineering, Civil Engineering, or related field
• Background in the foundations of solid mechanics, including strength of materials, continuum mechanics, and/or mechanics of materials
• Experience with a programming language such as C, C++, Julia, MATLAB, Python, and/or Rust
• Experience conducting research in scientific machine learning and implementing architectures, such as physics informed neural networks, graph neural networks and/or operator learning methods
• 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 clearance

• Experience performing mechanical finite element calculations
• Experience in constitutive model development, including viscoplastic materials
• Experience implementing material models in commercial finite element analysis codes
• Experience with agentic AI workflows using open and/or closed-source LLMs
• Knowledge of and/or experience with experimental methods for material characterization
• Experience with genetic algorithms (GAs) and specialized GA libraries

The Materials and Failure Modeling Department performs…