Postdoctoral Scholar - Reinhart Group - Materials Science and Engineering

Penn State University • University Park, PA

1d • Onsite

About The Position

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

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

Nice To Haves

• Deep learning for image analysis
• Multimodal or multi-task learning
• Deployment of ML 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 for AI systems to interpret scientific data from instruments.
• Deploy machine learning models on edge-computing infrastructure for real-time feedback.
• Integrate machine learning models with the Lifetime Sample Tracking data platform.
• Collaborate with domain scientists in thin-film growth and characterization.
• Collaborate with AI researchers developing the agentic workflow framework.
• Collaborate with future users of the LATTICE framework.

Benefits

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

What This Job Offers

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