Postdoc: Learning-Enabled 3D Imaging & Multimodal Fusion

Argonne National Laboratory; United States; On-site; USD 72,879 - 121,465; Full time

Job summary

Argonne National Laboratory invites applications for a Postdoctoral Appointee in the Computational Science and Artificial Intelligence Group within the X-ray Science Division of the Advanced Photon Source to advance learning-enabled imaging methods.

The role focuses on developing learning-enabled imaging methods for guiding data collection and analyzing synchrotron datasets, spanning from real-time X-ray measurements to post-experiment reconstruction in collaboration with physics, biology, and

Qualifications

• 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 ability to implement and debug research-grade algorithms.
• Demonstrated ability to tackle complex data analysis problems and deliver robust computational solutions.
• Excellent communication skills and strong interest in interdisciplinary collaboration.
• Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.

Responsibilities

• 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 model microbial organization across scales.

Skills

Machine learning

Computational imaging

Computer vision

Signal processing

Education

PhD in Electrical Engineering/Computer Science/Applied Mathematics/Physics

Job description

Argonne National Laboratory invites applications for a Postdoctoral Appointee in the Computational Science and Artificial Intelligence Group within the X-ray Science Division of the Advanced Photon Source to advance learning-enabled imaging methods.

The role focuses on developing learning-enabled imaging methods for guiding data collection and analyzing synchrotron datasets, spanning from real-time X-ray measurements to post-experiment reconstruction in collaboration with physics, biology, and