This work will focus on the application of materials informatics and analytics methods, including uncertainty quantification, to materials science and engineering problems relevant to ongoing research efforts in the Material Measurement Laboratory at NIST. The successful applicant will be expected to research, evaluate, and apply materials informatics and data science methods, including data processing, reproducible analysis, and clear reporting (e.g., visualization methods) in an interdisciplinary environment that includes both experimentalists and computational materials researchers, as well as experts in other domains such as computer science and statistical engineering. Proposals that lead to tools and methods for improved uncertainty quantification (including Bayesian analysis) and propagation in materials design applications are particularly encouraged. Machine learning, scripting and automation, and high performance computing (HPC) skills are desired.

Particular areas of interest include mechanical deformation, x-ray diffraction, neutron diffraction and computational methods (such as atomistics or Calphad modeling) as applied to metallic and polymer systems; multicomponent diffusion in metallic systems; composites; and additive manufacturing. Other materials systems are also of potential interest, depending on the expertise of the applicant.

key words

materials informatics; experimental optimization; materials design; materials data science; uncertainty quantification; Monte Carlo methods; atomistics; Calphad; diffusion; mechanical deformation; additive manufacturing

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants