Job Title: Postdoctoral Appointee - Scientific Machine Learning

Location of Position: Albuquerque, New Mexico

Employing Institution: Sandia National Laboratories

Description of Position: We seek a postdoctoral appointee to apply state-of-the-art scientific machine learning tools to develop data-driven approaches to efficient control and diagnostics of additive manufacturing and electrochemical processes of thin films. The successful candidate will work with a diverse team of modelers, experimentalists and applied mathematicians to develop a machine learning framework for material science problems. We are committed to nurturing a culture compatible with a broad group of people and perspectives in accordance with the changing makeup of the workforce. In support of this vision, the center actively recruits applicants from diverse groups of backgrounds and fosters an inclusive community. In this role, you will work collaboratively on a multidisciplinary research team conducting fundamental algorithmic research.

On any given day, you may be called on to:

  • Conduct leading-edge research in Scientific Machine Learning (SciML), including both physics-informed techniques incorporating engineering/physics models and traditional image analysis of high throughput material science experiments
  • Work towards publishing new developments in high-profile peer-reviewed scientific journals or refereed conference proceedings; contribute to development of open-source software for high performance computing environments
  • Interact with a diverse set of colleagues from both your own field, applications specialists, and others
  • Travel as needed to support projects
  • Work with export-controlled information which requires US Person status

Minimum Qualifications: 

  • Possess, or are pursuing, a PhD in mathematics, material science, physics, computer science, or a related engineering or natural science field (conferred within 3 years prior to employment)
  • Familiarity with optimization or deep learning, as evidenced by either completion of a graduate class that covered optimization or deep learning or use of optimization or deep learning in a research setting.
  • Training in continuum modeling using differential equations, with particular preference for those with training in numerical solution of differential equations for surface physics.

Due to U.S. export-control laws, only U.S. Persons (U.S. citizens, lawful permanent residents, asylees, or refugees) are eligible for consideration.

Application Procedure: Please apply at