Postdoc - AI-Accelerated Diffraction Imaging of Complex Materials

The Computational X-ray Science (CXS) group at the Advanced Photon Source (APS) ( is involved in developing computational solutions for materials characterization using x-rays. CXS supports a wide variety of advanced scientific instruments at the APS by developing new algorithms, AI/ML models and building software frameworks for x-ray techniques of diffraction, imaging and spectroscopy, as well as variants and combinations of these base techniques.

CXS is looking for a post-doctoral appointee to develop AI/ML techniques for automated data analysis of high energy x-ray tomographic data using both diffraction and absorption-based contrast. The successful candidate will work to accelerate processing and feature detection of this multi-modal 3D data, including use of automated morphology quantification. These computational developments are aimed at enabling studies of larger numbers of samples as well as more complex samples - which may contain multiple phases and several tomographic features - than possible today.

Candidates with a background in machine learning, computational materials science, computational physics, image processing, inverse problems, applied math and x-ray science are encouraged to apply.

Position Requirements

Required Skills and Experience
  • Ph.D. Degree and 0-1 years of experience OR
  • Master's Degree and 2-3 years of experience OR
  • Bachelor's Degree and 6-8 years of experience.

Desired Skills and Experience
  • Experience in developing AI/ML models applied to Materials Science or Physics problems.
  • Knowledge of computation tools for development of software including programming languages such as Python.
  • Skill with machine learning frameworks including scikit-learn, Tensorflow and PyTorch.
  • Knowledge of diffraction, crystallography and microtomography
  • Ability to work in a team environment.
  • Good written and communication skills.

Questions about the posting should be directed to

Job Family
Postdoctoral Family

Job Profile
Postdoctoral Appointee

Worker Type
Long-Term (Fixed Term)

Time Type
Full time

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