Statistics (LANS), Mathematics and Computer Science Division at Argonne National Laboratory invite applicants for a postdoctoral position in the area of utilizing machine learning for large-scale ptychographic experiments, an essential tool for high-resolution and nondestructive material characterization (X-ray and electron imaging). Postdoctoral appointees will investigate and develop semi-supervised, unsupervised, and active learning algorithms to understand object structure from scanning diffraction patterns, facilitate smart data collection, and create optimal experimental designs. Candidates interested in applying developments in deep clustering and active learning for challenging large- scale data analysis in importation applications will find a stimulating environment.