We are seeking a NIST postdoctoral Fellow within the  Materials Measurement Science Division. This postdoc will be a key member of a new project to develop an autonomous platform for developing and testing novel MOF materials for applications in carbon capture (https://doi.org/10.1016/j.xcrp.2022.101063). Successful candidates must have a background in MOF synthesis and characterization. Special consideration will be made to candidates with experience in automation or machine learning.

The postdoc will join a group which is focused on pioneering applications of modern machine learning methods, FAIR data infrastructure, and experimental automation to materials characterization (metrology) methods across all portions of the structure-processing-properties-performance relationship. Specific group research focus areas include the development of interpretable and trustworthy algorithms for Scientific Artificial Intelligence and active learning, integrating FAIR data management practices throughout the research lifecycle, and developing innovative on-demand material synthesis and characterization platforms for closed-loop materials development.

key words

Metal organic framework; Chemistry automation; active learning; synthesis; direct air capture; ftir; x-ray scattering;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants