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Postdoctoral Appointee - Synchrotron Studies of Crystal Defects for AI Modeling

Argonne National Laboratory · Lemont, IL · $70,758–$117,925 a year · Full time

Posted Nov 5, 2025 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about a quantitative reconstruction you did from X-ray data, such as phase retrieval or crystal truncation rod analysis.

What they are checking: Whether they can do the quantitative analysis the posting calls for, not just collect data.

Tell me about leading an experimental campaign. How did you plan and divide the work?

What they are checking: Whether they can lead experiments, a stated part of this role.

What would you do

An AI team wants you to collect data in a way that doesn't make sense physically. What do you do?

What they are checking: Whether they can push back with physics reasoning while still serving the larger goal.

What do you think an AlphaFold for Microelectronics would need to work, and what might make it fail?

What they are checking: Whether they think critically about the larger project they would be feeding.

About the work

Which synchrotron techniques would you use to measure defects at an oxide interface, and what does each tell you?

What they are checking: Whether they can pick from methods like surface scattering, microdiffraction and spectroscopy for a specific question.

What makes a dataset AI-ready, compared to a dataset that's good enough for a paper?

What they are checking: Whether they understand consistency, metadata, uncertainty and coverage needed for training models.

How would you align X-ray data with electron microscopy data from another facility on the same sample?

What they are checking: Whether they can think through registration and interoperability across multimodal data.

How would you design an experiment to watch defects change under operating conditions?

What they are checking: Whether they can plan in-situ or operando studies that connect defects to performance.

How would you study defects in 2D materials differently from bulk oxide films?

What they are checking: Whether they understand the different challenges of very thin and layered materials.

How do you document uncertainty in your measurements so a model can use it?

What they are checking: Whether they understand that models need error bars, not just values.

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The full job description

As published by Argonne National Laboratory.

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. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range for this position is $70,758.00-$117,925.00. 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. Click here to view Argonne employee benefits! As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law. Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department. All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

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