Postdoctoral Appointee - Machine Learning for Computational X-Ray Science - Military Veterans

at Argonne National Laboratory

Lemont, Illinois

The Advanced Photon Source (APS) is one of the most productive X-ray light source facilities in the world and conducts a wide range of cutting-edge research from battery materials to COVID-19 vaccines. The upcoming APS upgrade will further increase its source brightness and enable more applications with higher resolution and imaging rate. To achieve this goal, the X-Ray Science Division is seeking a postdoctoral candidate to develop robust and effective algorithms to automate data analysis and experimental control in computational microscopy including tomography, ptychography, and fluorescence imaging. The work involves utilizing state-of-the-art machine learning techniques to optimize parameters for experimental data acquired at the APS. The candidate will be part of a multidisciplinary team of beamline scientists, mathematicians, and software engineers.

Position Requirements

Education and Experience Requirements

  • Recent or soon-to-be completed PhD (typically completed within the last 0-3 years) in statistics, applied mathematics, physics, or related scientific or engineering field with a strong emphasis on machine learning.
  • Expertise in Python or other scientific programming languages
  • Demonstrated strong analytical and problem-solving skills
  • Effective verbal and written communication skills
  • Mixture of experience and interest in learning the following topics: image processing, black-box optimization, recurrent neural network, dimension reduction and statistical analysis of high-dimensional datasets

Desired but not required knowledge
  • Computational microscopy such as tomography and coherent diffraction imaging
  • Experience one of the following: Keras, Pytorch, Tensorflow
  • Experience in software development

Job Family
Postdoctoral Family

Job Profile
Postdoctoral Appointee

Worker Type
Long-Term (Fixed Term)

Time Type
Full time

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Lemont, Illinois

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