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Postdoctoral Appointee - AI for Synchrotron Imaging
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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Practice this out loudThe full job description
As published by Argonne National Laboratory.