We research and develop mathematical and statistical analysis for uncertainty quantification in scientific computing. Research focus areas include:

1. Materials science including atomistic and continuum techniques.
2. Magnetic resonance imaging for biomedical applications.
3. Neural networks including both traditional and neuromorphic architectures.

This work is performed in close collaboration with NIST scientists to provide innovative analysis and simulations in support larger strategic programs. Computational tools must be accompanied by statements of their uncertainties in their outputs, and sensitivities to inputs to serve as consistent extensions of measurement uncertainty analyses as defined by NIST and its stakeholders. Research teams are multi-disciplinary.

key words

Uncertainty quantification; Materials science; Machine learning; Neural networks; Computational physics; Numerical analysis;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants