The Advanced Photon Source (APS) (https://www.aps.anl.gov/) at Argonne National Laboratory (Lemont, Illinois, US (near Chicago)) invites applicants for a postdoctoral position to build a physics-informed AI framework that decodes the complex relationships between material defects, functional fields (e.g., strain, electrostatic potential), and device performance, with a primary focus on leveraging advanced synchrotron X-ray techniques.
The successful candidate will be at the forefront of integrating cutting-edge AI methodologies with world-leading X-ray characterization. You will develop and apply novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg Coherent Diffraction Imaging (BCDI), ptychography, and X-ray Photon Correlation Spectroscopy (XPCS). The goal is to move beyond simple correlations to discover the causal, governing rules of defect-property relationships in next-generation electronic materials.
This role involves creating AI models for real-time data analysis, enabling autonomous experiments through active learning and "curiosity-driven" exploration, and contributing to a robust data infrastructure that makes large-scale, multimodal datasets FAIR and AI-ready. You will publish findings in high-impact journals, present at major international conferences, and work within a large, interdisciplinary team of experts from multiple national laboratories and universities.
The appointee will benefit from direct access to the unique capabilities of the upgraded APS, leadership-class computing at the Argonne Leadership Computing Facility (ALCF), and state-of-the-art microscopy at the Center for Nanoscale Materials (CNM). Candidates with a strong background in computational science, machine learning, and experience with synchrotron data analysis are strongly encouraged to apply.
Position Requirements
PhD completed in the past 5 years or soon to be completed in a relevant field of study.
Experience with deep learning (DL) frameworks such as PyTorch, TensorFlow, or JAX.
Strong programming proficiency in Python.
Demonstrated experience with coherent X-ray techniques (e.g., BCDI, ptychography, XPCS) and associated data analysis.
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 scientific AI techniques like Physics-Informed Neural Networks (PINNs) and geometric deep learning.
Experience with active learning, agentic workflows, or other methods for autonomous experimentation.
Familiarity with high-performance computing (HPC) environments and large-scale data management.
Experience with version control (e.g., Git) and collaborative software development practices.
Experience with real-time analysis and instrument control at a user facility.
Skill in both written and oral scientific communication.
A strong track record of working collaboratively in a diverse, team-oriented research environment.
Job Family
Postdoctoral
Job Profile
Postdoctoral Appointee
Worker Type
Long-Term (Fixed Term)
Time Type
Full timeThe 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.
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