Develop methods of applying machine learning and artificial intelligence to synchrotron experimentation. This opportunity will be focused on operations at NIST's Beamline for Materials Measurement at the National Synchrotron Light Source II, but might include measurements at other NSLS-II beamlines. The duty station would be at Brookhaven National Laboratory with the Synchrotron Science Group and involve occasional travel to Gaithersburg, MD.

The candidate would be responsible for planning and performing high-throughput XAS, XRF, and XRD measurements mostly at NIST’s Beamline for Materials Measurements with the intent of develpoing methods to leverage machine learning and artifical intelligence tools to enhance and automate beamline operations.  Some example applications might include evaluation of data quality, assistance to beamline and experimental configuration, data analysis worklows, and integration into multi-model and multi-beamline measurement systems.

Ths position requires a deep understanding of X-ray Absoprtion Spectroscopy and prior experience with methods of machine learning and artificial intelligence. A highly competitive candidate would also have familiarity with X-ray Diffraction and Pair Distribution Function anaylisis, experience with data acquisition using the Bluesky experiment orchestration package, and knowledge of beamline controls. The position will require programming skills, mostly with Python.

To understand the vision of beamline operations inspiring this opportunity, see these two recent publications: https://doi.org/10.1016/j.xcrp.2022.101112 and https://doi.org/10.1080/08940886.2022.2114716

key words

synchrotron radiation; X-ray Absorption Spectroscopy, machine learning, artificial analysis, autonomous experimentation

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants