Postdoctoral Scholar - Reinhart Group - Materials Science and Engineering

Penn State University • University Park, IL

3d

About The Position

The Department of Materials Science and Engineering in Penn State’s College of Earth and Mineral Sciences seeks a postdoctoral scholar to work on machine learning for autonomous thin-film materials synthesis. The work is part of LATTICE (Layered and Thin Film Technologies with Intelligent Cloud Experimentation), a National Science Foundation Programmable Cloud Laboratory at Penn State’s University Park campus.

The scholar will develop specialized machine learning models that enable AI systems to interpret scientific data from instruments such as atomic force microscopy, reflection high-energy electron diffraction, spectroscopic ellipsometry, and X-ray diffraction. These models will function as the perceptual layer for a multi-agent AI framework that guides autonomous materials synthesis and interacts with robotic systems. Models will be trained, calibrated, and validated using the data modalities produced by LATTICE instrumentation. The scholar will deploy models on edge-computing infrastructure for real-time feedback during deposition and integrate them with the Lifetime Sample Tracking data platform.

The role involves close collaboration with LATTICE domain scientists working in thin-film growth and characterization, AI researchers developing the agentic workflow framework, and future users of the LATTICE framework. This is a term position; the term length will be discussed during the interview. Continuation beyond the discussed term depends on university need, performance, and/or funding availability.

Requirements

• Ph.D. in Materials Science and Engineering, Computer Science, Electrical Engineering, Physics, or a related field.
• Strong research experience in applied machine learning, computer vision, and/or scientific data analysis.
• Extensive programming experience in Python.

Nice To Haves

• Experience with deep learning for image analysis.
• Experience with multimodal or multi-task learning.
• Experience deploying machine learning models in real-time or edge-computing environments.
• Domain knowledge of materials science, chemistry, or physics.
• Familiarity with PyTorch or JAX.

Responsibilities

• Develop specialized machine learning models to interpret scientific data from LATTICE instruments.
• Train, calibrate, and validate models against data modalities produced by LATTICE instrumentation.
• Deploy models on edge-computing infrastructure to provide real-time feedback during deposition.
• Integrate models with the Lifetime Sample Tracking data platform.
• Collaborate with LATTICE domain scientists, AI researchers, and future users of the LATTICE framework.

Benefits

• Competitive benefits package for full-time employees designed to support personal and professional well-being.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree