Postdoctoral Appointee - X-ray Scattering Studies of Catalytic Materials AI-Enabled Discovery

We are seeking a Postdoctoral Research Associate to join the Structural Science Group within the X-ray Science Division (XSD) at the Advanced Photon Source (APS), Argonne National Laboratory. This position is part of the DOE-BES initiative Integrated Scientific Agentic AI for Catalysis (ISAAC), a multi‑facility collaboration integrating experimental measurements, simulations, and data science to enable physics‑informed AI agents that accelerate discovery in catalysis science.

The successful candidate will focus on the development and application of machine learning and AI approaches to analyze synchrotron X‑ray scattering data, including X‑ray diffraction and total scattering measurements. The research will emphasize extracting physically meaningful descriptors from complex scattering datasets and integrating these results into the ISAAC multimodal data ecosystem to support autonomous scientific discovery.

The postdoctoral researcher will contribute to the development of AI‑ready data pipelines and analysis frameworks that enable rapid interpretation of scattering measurements and facilitate the training of intelligent agents capable of guiding experiments and simulations in catalytic materials research.

• Develop and apply machine learning and data‑driven methods for the analysis and interpretation of synchrotron X‑ray scattering data, including diffraction and total scattering measurements.
• Design computational workflows that transform experimental data into AI‑ready descriptors suitable for integration within the ISAAC data infrastructure.
• Collaborate with beamline scientists and user groups to incorporate scattering datasets into multimodal AI frameworks combining experimental measurements and simulations.
• Contribute to the development of automated data processing and analysis pipelines for large‑scale scattering datasets.
• Participate in synchrotron experiments at APS beamlines to generate datasets that support algorithm development and validation.
• Work closely with ISAAC collaborators across institutions to enable integration of scattering‑derived insights into physics‑informed AI models.
• Document research results, contribute to publications in peer‑reviewed journals, and present findings at scientific conferences.

• Ph.D. completed within the past five years, or soon to be completed, in physics, materials science, chemistry, computer science, applied mathematics, or a related discipline.
• Demonstrated experience with X‑ray scattering methods, such as diffraction, total scattering, or related structural characterization techniques.
• Strong background in scientific programming and data analysis (Python, MATLAB, or equivalent).
• Experience with machine learning, data‑driven modeling, or AI methods applied to physical sciences datasets.
• Familiarity with handling and analyzing large or complex experimental datasets.
• Ability to work effectively within a multidisciplinary, multi‑institutional collaboration.
• Excellent written and oral communication skills.
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

• Experience applying machine learning or AI techniques to scattering, imaging, or spectroscopy datasets.
• Familiarity with total scattering analysis, pair distribution function (PDF) methods, or diffraction data analysis.
• Experience with automated data pipelines, high‑throughput analysis, or real‑time data processing for large experimental datasets.
• Familiarity with multimodal data integration and scientific data management frameworks.
• Interest in the development of AI‑guided or autonomous experimentation frameworks.

The expected hiring range for this position is $72,879.00–$. 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…