This research opportunity involves development of computational approaches to model the state and response of complex dynamical systems under uncertainty. 

Candidates should have a background in modeling and numerical solution of response of complex multi-physics systems represented by parameterized partial differential equations. Knowledge of mathematical approaches to address uncertainty in such systems is a key need. Experience with combining modern data analytics methods, such as machine learning and deep learning, with physics-based reduced order model, using principal component analysis or similar techniques, is desired. Ability to design, implement and verify and validate algorithms is expected.

key words

Parameterized partial differential equations; reduced order modeling; principal component analysis; proper orthogonal decomposition; uncertainty modeling and quantification; digital thread; digital twin; inverse problems; parameter estimation

Citizenship:  Open to U.S. citizens and permanent residents

Level:  Open to Postdoctoral applicants