Argonne

Argonne

Postdoctoral Appointee - AI for Synchrotron Imaging

Lemont, IL USA · Contract

Sponsorship not specified$73k-$121kDetected 50 days ago
AlgorithmsMachine LearningData AnalysisComputer VisionElectrical EngineeringSignal ProcessingResearchCommunicationCollaboration

About the role

  • Advance multimodal analysis methods that align and fuse structural, chemical, and biological signals to construct coherent models of microbial organization across scales.
  • Strong expertise in machine learning, computational imaging, computer vision, or signal processing.

Responsibilities

  • The APS at Argonne National Laboratory is a world-leading synchrotron facility recently upgraded to deliver nanometer-to-micron resolution imaging with dramatically increased X-ray flux.
  • Implement adaptive acquisition strategies that guide beamline measurements in real time to increase efficiency and improve image quality.
  • Demonstrated ability to work on complex data analysis problems and deliver robust computational solutions.

Requirements

  • Ph.D. completed in the past 5 years or soon-to-be completed in Electrical Engineering, Computer Science, Applied Mathematics, Physics, or a related field.
  • Proficiency in scientific programming and modern ML frameworks, with the ability to implement and debug research-grade algorithms.
  • Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.
  • Interpersonal skills, oral and written communication skills, and ability to interact with people at all levels both within and outside the laboratory.
  • Experience with synchrotron or tomographic imaging datasets.

Nice to have

  • Preferred Knowledge, Skills, and Experience

Skills

  • Excellent communication skills and a strong interest in interdisciplinary collaboration.

Compensation

  • Please note that the pay range information is a general guideline only.

Benefits

  • Develop learning-enabled algorithms for 3D reconstruction of noisy and heterogeneous synchrotron datasets.
  • Background in inverse problems or physics-informed machine learning.
  • This position focuses on developing learning-enabled imaging methods to guide data collection and analyze synchrotron datasets, spanning the full experimental cycle from real-time

This listing is sourced directly from Argonne's careers page and normalized into a canonical job model.