Advanced computational and statistical techniques are required to extract maximal information from measurements that arise in experiments investigating inorganic materials. The development of models for the simultaneous description of data from a variety of measurement sources (e.g., transmission electron microscopy, x-ray and neutron diffraction, x-ray absorption fine structure, and Raman spectroscopy) provides insight into the underlying physical system and allows the determination of atomic positions in nanostructured materials. Advances in computational statistics methodology and algorithms for numerical optimization allow model parameters to be estimated precisely and with minimal overhead in terms of computation time and memory space. Quantification of uncertainties associated with model parameters and measurements pertaining to Standard Reference Materials facilitates an assessment of experimental limitations. Uncertainty quantification also guides model refinements and improvements in experimental procedure. Open-source software enabling statistical model formulation, estimation of free model parameters, and uncertainty quantification is released in the public domain. Interaction with communities devoted to the development of open-source software for data analysis and modeling, such as the R project for statistical computing, contributes to the standardization and dissemination of research results.

 

Keywords:

Computational crystallography; Computational physics; Computational statistics; Data analysis; Optimization; Scientific computing; Statistical inference; Uncertainty quantification;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants