Postdoctoral Appointee - Synchrotron Studies of Crystal Defects for AI Modeling
Core
Lead experimental campaigns using synchrotron X-ray techniques to generate AI-ready datasets revealing defect-mediated mechanisms in thin films and heterointerfaces for a physics-informed AI framework.
Role type
Postdoctoral Researcher (Synchrotron Characterization & AI Data Generation)
Builds
Multimodal datasets linking composition, structure, and operating conditions to defect evolution in microelectronics materials.
Domain
Materials Science / Microelectronics / Synchrotron Physics / AI/ML
Deliverable
production ML models
Required skills
Synchrotron X-ray methods (BCDI, XPCS, ptychography, Laue microdiffraction), experimental design, quantitative data analysis, scientific programming (Python, MATLAB), in-situ/operando experiment execution, data fusion
Preferred skills
In-situ measurements under electrical/thermal bias, multimodal data integration, AI/ML data structures knowledge, materials physics in thin films/2D materials
Technologies
BCDI, XPCS, ptychography, Laue microdiffraction, Python, MATLAB
Responsibilities
Design and perform advanced synchrotron experiments to probe structural and dynamic evolution of defects; Utilize coherent imaging techniques to study strain, dislocation networks, and interfacial morphology; Develop in-situ and operando experiments under bias to capture real-time defect dynamics; Integrate multimodal datasets and collaborate with AI/ML teams for data fusion and model validation; Publish high-impact research results and present findings at conferences.
Seniority
Postdoctoral, hands-on IC
