Postdoctoral Appointee: AI​/ML Uncertainty Quantification, Onsite

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We are seeking a highly motivated and driven Postdoctoral Appointee to join a multidisciplinary team conducting research at the intersection of computational science, applied mathematics, and data‑driven modeling. The selected candidate will have the opportunity to contribute to two complementary research efforts: (i) developing AI/ML methods for uncertainty quantification in multiscale materials modeling and (ii) developing domain decomposition‑based hybrid modeling approaches that couple full‑order, reduced‑order, and data‑driven models.

For the first research effort, the selected candidate will contribute to 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.

For the second research effort, the selected candidate will conduct research on domain decomposition‑based approaches for hybrid modeling and simulation. This work will focus on developing methods for coupling models of different fidelities and/or mathematical representations including full‑order models (FOMs) and data‑driven reduced‑order models (ROMs) within a common domain decomposition framework. A particular area of interest is the development of adaptive approaches that enable online switching between ROMs and FOMs within individual subdomains as solution features evolve during a simulation, with the goal of balancing computational efficiency and predictive accuracy.

On a typical day, you may be called on to develop and implement algorithms related to the creation of accurate and efficient adaptive hybrid models, applied primarily to solid mechanics exemplars.

The selected candidate will work closely with Sandia Principal Investigators and will also collaborate with scientists from other national laboratories and universities involved in these projects.

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 projection‑based reduced order modeling and/or operator inference.
• Knowledge and expertise in computational science and/or software development.
• This position requires access to export‑controlled or ITAR information. Only U.S. persons (citizens, lawful permanent residents, asylees or refugees) are eligible for consideration.

• Proficiency in using open‑source machine learning libraries such…