The X-ray Imaging Group (https://www.aps.anl.gov/Imaging) of the X-ray Science Division (XSD) operates the full-field x-ray imaging beamlines at the Advanced Photon Source (APS) (https://www.aps.anl.gov/) and pursues collaborative research across materials, energy, environmental, geo-, and life sciences using micro- and nano-tomography and high-speed imaging.
With the APS Upgrade driving a substantial increase in data rates and experimental complexity, the group is investing in AI/ML-driven software, automation, and autonomous experimentation, including a planned fully autonomous, AI-driven tomography beamline — to keep pace with user demand and unlock new science. This work is carried out in close coordination with the APS Computational science and AI (CAI) (https://cai.xray.aps.anl.gov/) group and other APS AI efforts and activities spanning data, computing, and machine learning across the facility.The appointee will benefit from access to world-leading experimental and computational resources at Argonne, including the upgraded APS and the exascale Aurora supercomputer.
We seek highly motivated candidates with strong expertise in AI/ML and in the development of AI tools for scientific operations to join the X-ray Imaging Group. The successful candidate will lead the design and deployment of AI-powered software that supports user operations, automates data acquisition and analysis, and enables closed-loop, autonomous experiments across the group's beamlines.
Position Responsibilities:
Design, develop, and deploy AI/ML tools for x-ray imaging operations including reconstruction, super-resolution, spatiotemporal fusion, denoising, segmentation, and feature extraction — and integrate them into the beamline software stack.
Build closed-loop experimental workflows in which AI agents use streaming data and real-time reconstruction and analysis to steer measurement decisions, and contribute to the development of a fully autonomous, AI-driven tomography beamline as a flagship project for the group.
Collaborate with the APS Computation and AI (CAI) group and engage with other APS AI efforts and activities to align Imaging Group tools with facility-wide AI/ML infrastructure, data services, and computing resources, and to contribute to shared frameworks for autonomous experimentation.
Develop automated pipelines for acquisition, quality control, and downstream analysis that translate beamline-scientist expertise and currently manual operational steps into robust, reusable software.
Present research results through publications, conferences, and scientific meetings.
May be required to perform other duties as assigned.
Position Requirements
Ph.D. in computer science, electrical engineering, computational physics, computational materials science, applied mathematics, or a closely related field.
Demonstrated expertise in AI/ML applied to imaging or scientific data, including hands-on experience developing and deploying deep-learning models (e.g., CNNs, vision transformers, diffusion models, or related architectures).
Strong scientific software development skills in Python and modern deep-learning frameworks (e.g., PyTorch, TensorFlow), including experience with distributed training on high-performance computing resources.
Experience working with experimental imaging data, ideally at a synchrotron, electron microscopy, medical imaging, or comparable facility.
Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.
Preferred Knowledge, Skills, and Experience
Experience designing or contributing to automated, remote, or closed-loop ("self-driving") experimental workflows, including real-time data reduction, on-the-fly reconstruction, and AI-based experimental steering.
Familiarity with beamline data acquisition systems, detectors, or controls software, sufficient to integrate AI tools into operational workflows.
Job Family
Research Development (RD)
Job Profile
Physics 2
Worker Type
Regular
Time Type
Full timeThe expected hiring range for this position is $94,486.00 - $147,398.94.
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.
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