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

Additional Benefits

Relocation

Awardees who reside more than 50 miles from their host laboratory and remain on tenure for at least six months are eligible for paid relocation to within the vicinity of their host laboratory.

Health insurance

A group health insurance program is available to awardees and their qualifying dependents in the United States.