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Postdoctoral Appointee - AI for Synchrotron Imaging

Argonne National Laboratory · Lemont, IL · $72,879–$121,465 a year · Full time

Posted May 28, 2026 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about a time you worked with scientists from another field, like biology or environmental science.

What they are checking: Whether they can learn enough of a partner's field to solve the right problem for them.

Tell me about the research project you're proudest of and what you specifically contributed.

What they are checking: The depth of their own technical contribution and how clearly they explain it.

What would you do

Your model produces clean-looking images, but a biologist thinks the features aren't real. What do you do?

What they are checking: Whether they take hallucination and artifact risk seriously and know how to validate results.

How would you make sure your methods work during a live beamtime, not just offline?

What they are checking: Whether they think about speed, reliability and integration with beamline control systems.

What would you do in your first few months to get up to speed on the imaging instruments here?

What they are checking: Whether they have a realistic plan to learn the beamline before building on top of it.

About the work

How would you use a learned model to decide where to scan next on a soil sample, instead of scanning everything?

What they are checking: Whether they understand adaptive, learning-guided data collection, which is the heart of this role.

Soil is opaque and messy. What makes reconstructing microbial structure from synchrotron data hard, and how would you approach it?

What they are checking: Whether they grasp the imaging physics and noise problems specific to this research context.

How would you combine a physics-based forward model with a neural network for image reconstruction?

What they are checking: Whether they know physics-informed or model-based deep learning instead of treating imaging as a black box.

The upgraded APS gives much higher flux. How does that change the data volume problem, and how would you handle it?

What they are checking: Whether they can think about real-time processing and data reduction at the scale the upgrade creates.

How would you train a model when you have very little labeled ground truth from real soil samples?

What they are checking: Whether they know self-supervised, simulated or transfer learning approaches for scarce labels.

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The full job description

As published by Argonne National Laboratory.

Position Overview We are seeking a Postdoctoral Appointee to join the Computational Science and Artificial Intelligence Group in the X-ray Science Division of the Advanced Photon Source (APS) at Argonne National Laboratory to advance learning-enabled imaging methods. This position offers a unique opportunity for candidates with backgrounds in electrical engineering, computer science, applied mathematics, or physics to apply their expertise to challenging problems in computational imaging, while collaborating with leading experts in physics, biology, and environmental science. Research Context Soil microbial communities play a fundamental role in carbon and nutrient cycling, yet their spatial organization and interactions have remained difficult to study because of the opacity and complexity of soil. 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. This makes it possible to visualize the interplay of soil structure and microbial life at scales bridging nanometers to millimeters, creating a unique opportunity to investigate how microbial communities are organized and interact within their natural environments. Your Role 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 X-ray measurements to post-experiment reconstruction: • Develop learning-enabled algorithms for 3D reconstruction of noisy and heterogeneous synchrotron datasets. • Implement adaptive acquisition strategies that guide beamline measurements in real time to increase efficiency and improve image quality. • Advance multimodal analysis methods that align and fuse structural, chemical, and biological signals to construct coherent models of microbial organization across scales. Success in this role will require creativity in computational imaging, machine learning, and signal processing, as well as close collaboration with experts in computational science, electrical engineering, synchrotron physics, soil microbiology, and environmental chemistry. May be required to perform other duties as assigned. Position 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. • Strong expertise in machine learning, computational imaging, computer vision, or signal processing. • Proficiency in scientific programming and modern ML frameworks, with the ability to implement and debug research-grade algorithms. • Demonstrated ability to work on complex data analysis problems and deliver robust computational solutions. • Excellent communication skills and a strong interest in interdisciplinary collaboration. • 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. Preferred Knowledge, Skills, and Experience • Experience with synchrotron or tomographic imaging datasets. • Background in inverse problems or physics-informed machine learning. • Exposure to scientific imaging applications (for example, biological, environmental, or materials science). Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range for this position is $72,879.00-$121,465.00. Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package. Click here to view Argonne employee benefits! As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law. Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department. All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

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