Postdoctoral Appointee - AI​/ML Uncertainty Quantification, Onsite

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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:

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We are seeking a highly motivated and driven postdoctoral appointee to join a multidisciplinary team focused on a multiscale effort to predict metallic microstructure evolution and mechanical failures in extreme environments. Ideal candidates will be creative problem solvers with a solid foundation in uncertainty quantification, AI/ML, and materials modeling, along with experience in scientific computing, as demonstrated by relevant publications and code contributions.

On any given day, you may be called on to: conduct research to develop effective supervised and unsupervised learning algorithms to facilitate large‑scale computational studies of phase field and polycrystalline structure evolution. The selected candidate will work closely with Sandia Principal Investigators and will also collaborate with scientists from other national laboratories and universities involved in the project.

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

• PhD in a field of physical sciences, applied mathematics, engineering, or other relevant field conferred within five years prior to employment.
• Knowledge and expertise in machine learning and/or uncertainty quantification.
• Knowledge and expertise in computational science and/or software development.

• Proficiency in using open‑source machine learning libraries such as PyTorch or JAX for developing, training, and deploying advanced machine learning models tailored to complex scientific problems.
• Expertise in operator learning and generative models with a focus on their applications in the physical sciences.
• Experience with reduced order modeling and latent space representations.
• Strong programming skills in C++ and Python and integration of machine learning solutions into existing scientific workflows.
• Collaborative research experience.
• Excellent interpersonal and communication skills.

The Quantitative Modeling and Analysis Department conducts research, development, and systems engineering in computer science and engineering to address important, complex national security problems. Key research areas include uncertainty quantification, optimization, inference modeling, data analysis, and algorithm development. Much of our application development work focuses on verification, validation and uncertainty quantification of complex problems. We provide trusted design and software development for large, operations software as well as detailed scientific computing software.

The work is focused on providing efficient, validated information and technologies for use in predictive modeling and decision making.

This posting will be open for application submissions for a minimum of seven (7) calendar days, including the 'posting date'. Sandia reserves the right to extend the posting date at any time.

Sandia is required by DOE to conduct a pre‑employment drug test and background review that includes checks of personal references, credit, law enforcement records, and…