Postdoctoral Appointee: Physics-Informed AI for Microelectronics Materials

Responsibilities

• Design, implement, and validate physics-informed AI/ML models for microelectronics materials
• Curate, manage, and integrate heterogeneous datasets from experiments and simulations
• Collaborate closely with experimental teams to benchmark and refine computational models
• Disseminate research through publications, presentations, and open-source contribution
• Position Requirements
• Recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Data Science, Chemistry, Chemical Engineering, Electrical Engineering, Computer Science, Physics, or a related field
• Demonstrated proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow) applied to scientific problems
• Strong background in managing multimodal datasets
• Proven experience collaborating with experimental teams to validate computational models
• Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork

Requirements

• Deep understanding of AI/ML concepts, including transformers, latent-space representations, generative models, and reinforcement learning
• Experience with high-performance computing, physics-based simulations, and multimodal data workflows
• Demonstrated ability to train and deploy AI/ML models using simulated and experimental data
• Familiarity with agentic LLM-based approaches and related technologies (e.g., RAG, MCP, A2A)
• Interest in interfacial phenomena and defect dynamics in materials across scales
• 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.
• Click here to view Argonne employee benefits!

Benefits

Additional Information

The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks an outstanding postdoctoral researcher to advance data-driven, physics-informed AI for microelectronics materials. Working within an interdisciplinary team, you will develop frameworks that connect atomistic features, mesoscale dynamics, and device-level performance. The effort will integrate heterogeneous data from simulations and experiments across scientific user facilities, leveraging data to understand complex material phenomena across scales.

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