Argonne National Laboratory
PostdoctoralAppointee-AIforSynchrotronImaging
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Postdoctoral Appointee - AI for Synchrotron Imaging at Argonne National Laboratory. Skills: Machine learning, Computational imaging, Signal processing, 3D reconstruction. Develop learning-enabled algorithms for 3D reconstruction. Implement adaptive acquisition strategies”
Industry & Context.
Complex data analysis problems
What They're Looking For.
Must Have
Ph. D. completed in past 5 years, Expertise in machine learning, Expertise in computational imaging, Expertise in computer vision, Expertise in signal processing, Proficiency in scientific programming, Proficiency in modern ML frameworks, Implement and debug research-grade algorithms, Work on complex data analysis problems, Deliver robust computational solutions, Excellent communication skills, Interest in interdisciplinary collaboration, Model Argonne’s core values, Interpersonal skills, Oral communication skills, Written communication skills, Interact with people at all levels
Nice to Have
Experience with synchrotron imaging datasets, Experience with tomographic imaging datasets, Background in inverse problems, Background in physics-informed machine learning, Exposure to scientific imaging applications
What You'll Do.
Develop learning-enabled algorithms for 3D reconstruction
Implement adaptive acquisition strategies
Advance multimodal analysis methods
Align and fuse structural signals
Fuse chemical signals
Fuse biological signals
Construct coherent models
How You'll Work.
Team & Collaboration
Interdisciplinary collaboration; Collaborate with experts
Communication Scope
Oral communication; Written communication
Full Job Description
_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 im
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