Postdoctoral Scholar - Reinhart Group - Materials Science and Engineering

This is a term position; length of the term will be discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding.

About the role

The Department of Materials Science and Engineering in the College of Earth and Mineral Sciences is searching for a postdoctoral scholar. The position will involve machine learning for autonomous thin-film materials synthesis, including representation learning for scientific data, multimodal data fusion, and real-time predictive modeling. This work will be performed as part of LATTICE (Layered and Thin Film Technologies with Intelligent Cloud Experimentation), a National Science Foundation Programmable Cloud Laboratory at the Pennsylvania State University’s University Park campus.

The postdoctoral scholar will develop specialized machine learning models that enable AI systems to interpret scientific data from instruments including atomic force microscopy, reflection high-energy electron diffraction, spectroscopic ellipsometry, X-ray diffraction, and similar characterization methods. These models serve as the perceptual layer for a multi-agent AI framework that will guide autonomous materials synthesis and interact with robotic systems. The models will be trained, calibrated, and validated against the specific data modalities produced by LATTICE instrumentation. The scholar will deploy these models on edge-computing infrastructure for real-time feedback during deposition and integrate them with the Lifetime Sample Tracking data platform. The position requires close collaboration with LATTICE domain scientists in thin-film growth and characterization, AI researchers developing the agentic workflow framework, and future users of the LATTICE framework.

Requirements

Candidates must have a Ph.D. in Materials Science and Engineering, Computer Science, Electrical Engineering, Physics, or a related field, plus a strong record of research experience in applied machine learning, computer vision, and/or scientific data analysis.

• Preferred experience with one or more of the following is strongly preferred: deep learning for image analysis, multimodal or multi-task learning, or deployment of ML models in real-time or edge-computing environments.
• Domain knowledge of materials science, chemistry, or physics will be an advantage.
• Extensive programming experience in Python is required, and familiarity with PyTorch or JAX is preferred.

Benefits

Penn State provides a competitive benefits package for full-time employees designed to support both personal and professional well-being. For more detailed information, please visit the Postdoctoral Benefits page.