Argonne
Postdoctoral Appointee - AI for Synchrotron Imaging
Lemont, IL USA · Contract
Sponsorship not specified$73k-$121kDetected 50 days ago
AlgorithmsMachine LearningData AnalysisComputer VisionElectrical EngineeringSignal ProcessingResearchCommunicationCollaboration
About the role
- Advance multimodal analysis methods that align and fuse structural, chemical, and biological signals to construct coherent models of microbial organization across scales.
- Strong expertise in machine learning, computational imaging, computer vision, or signal processing.
Responsibilities
- 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.
- Implement adaptive acquisition strategies that guide beamline measurements in real time to increase efficiency and improve image quality.
- Demonstrated ability to work on complex data analysis problems and deliver robust computational solutions.
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.
- Proficiency in scientific programming and modern ML frameworks, with the ability to implement and debug research-grade algorithms.
- 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.
- Experience with synchrotron or tomographic imaging datasets.
Nice to have
- Preferred Knowledge, Skills, and Experience
Skills
- Excellent communication skills and a strong interest in interdisciplinary collaboration.
Compensation
- Please note that the pay range information is a general guideline only.
Benefits
- Develop learning-enabled algorithms for 3D reconstruction of noisy and heterogeneous synchrotron datasets.
- Background in inverse problems or physics-informed machine learning.
- 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
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