Postdoctoral Appointee - Synchrotron Studies of Crystal Defects for AI Modeling

The Surface Scattering and Microdiffraction (SSM) group in the X-ray Science Division (XSD) at the Advanced Photon Source (APS), Argonne National Laboratory is seeking Two Postdoctoral Appointees, both focused on multimodal synchrotron characterization of defects and interfaces in oxides and 2D materials. These positions are part of a cross-facility initiative to build an “AlphaFold for Microelectronics”—a physics-informed AI framework that links composition, structure, and operating conditions to defect evolution and functional performance.

The successful candidates will lead experimental campaigns using advanced synchrotron X-ray techniques to generate quantitative, AI-ready datasets that reveal defect-mediated mechanisms governing the stability, adhesion, and transport behavior of thin films and heterointerfaces. The postdoc will lead experimental design, data acquisition, and quantitative reconstruction.

The appointees will work within a highly collaborative team spanning multiple DOE user facilities, who are developing complementary microscopy and AI/ML workflows, ensuring that multimodal datasets (X-ray, electron microscopy, and spectroscopy) are well-aligned and interoperable. These positions offer a unique opportunity to pioneer multimodal, physics-driven synchrotron research that bridges defect dynamics and functionality in emerging microelectronic materials.

Key Responsibilities:

• Design and perform advanced synchrotron experiments to probe structural, chemical, and dynamic evolution of defects in thin films and heterostructures.

• Utilize techniques such as Bragg coherent diffraction imaging (BCDI), Laue microdiffraction, ptychographic laminography, and X-ray photon correlation spectroscopy (XPCS) to study strain, dislocation networks, voids, and interfacial morphology.

• Develop in-situ and operando experiments under electrical, thermal, or mechanical bias to capture real-time defect dynamics.

• Integrate multimodal datasets and collaborate with AI/ML teams for data fusion, physics-informed model validation, and causal discovery of defect–property relationships.

• Publish high-impact research results and present findings at national and international conferences.

Position Requirements

• Ph.D. completed in the past five years or soon-to-be completed in physics, materials science, chemistry, engineering, or a related discipline.

• Demonstrated expertise in one or more synchrotron X-ray methods such as BCDI, XPCS, ptychography, Laue microdiffraction, or related coherent/imaging techniques.

• Proven ability to design, conduct, and analyze complex synchrotron experiments.

• Proficiency in scientific programming (Python, MATLAB, etc.) and quantitative data analysis.

• Excellent written and oral communication skills.

• Ability to work effectively in a collaborative, multi-institutional team environment.

• 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.

Preferred Knowledge, Skills, and Experience

• Experience with in-situ or operando measurements under electrical or thermal bias.

• Familiarity with multimodal data correlation or integration with microscopy/spectroscopy datasets.

• Awareness of AI/ML data structures and metadata practices for interoperable experimental data.

• Strong background in materials physics, thin films, or functional oxides/2D materials.

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Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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