Description

This postdoctoral research opportunity supports the development of machine learning (ML) and artificial intelligence (AI) methods to enhance synchrotron radiation experimentation. The research will primarily focus on operations at the National Institute of Standards and Technology (NIST) Beamline for Materials Measurement at the National Synchrotron Light Source II (NSLS-II), located at Brookhaven National Laboratory in Upton, NY. The position involves planning and performing high-throughput X-ray Absorption Spectroscopy (XAS), X-ray Fluorescence (XRF), and X-ray Diffraction (XRD) measurements, with the goal of leveraging AI/ML tools to automate and improve beamline operations.

Key responsibilities include:

• Developing AI/ML methods for data quality evaluation, beamline and experimental configuration assistance, and data analysis workflows.
• Integrating AI/ML approaches into multi-model and multi-beamline measurement systems.
• Performing high-throughput synchrotron measurements primarily at NIST’s beamline, with potential work at other NSLS-II beamlines.
• Collaborating with the Synchrotron Science Group at Brookhaven National Laboratory.
• Occasional travel to NIST facilities in Gaithersburg, MD.

The position requires a strong background in X-ray Absorption Spectroscopy and experience with machine learning and artificial intelligence techniques. Familiarity with X-ray Diffraction, Pair Distribution Function analysis, and data acquisition using the Bluesky experiment orchestration package is highly desirable. Programming skills, especially in Python, are essential.

For insight into the vision behind this opportunity, applicants are encouraged to review recent publications on autonomous beamline operations:

• https://doi.org/10.1016/j.xcrp.2022.101112
• https://doi.org/10.1080/08940886.2022.2114716