Postdoctoral Appointee - AI for Synchrotron Imaging
Core
Developing learning-enabled imaging methods to guide data collection and analyze synchrotron datasets for visualizing soil microbial communities.
Role type
Postdoctoral Researcher in Computational Imaging and Machine Learning
Builds
3D reconstruction algorithms, adaptive acquisition strategies, and multimodal analysis models for synchrotron imaging
Domain
Synchrotron physics, computational imaging, soil microbiology, environmental science
Deliverable
production ML models
Required skills
machine learning, computational imaging, computer vision, signal processing, scientific programming, modern ML frameworks
Preferred skills
synchrotron or tomographic imaging datasets, inverse problems, physics-informed machine learning
Technologies
modern ML frameworks
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; Advance multimodal analysis methods that align and fuse structural, chemical, and biological signals
Seniority
Postdoctoral, research-focused IC