Postdoctoral Scholar - Reinhart Group - Materials Science and Engineering

The Pennsylvania State University; Struble (PA); On-site; USD 50,000 - 65,000; Full time

Job summary

The Pennsylvania State University in University Park seeks a postdoctoral scholar to advance machine learning for autonomous thin-film materials synthesis within the LATTICE program. The role focuses on representation learning for scientific data, multimodal fusion, and real-time predictive modeling on edge computing infrastructure.

You will build ML models to interpret data from instruments such as AFM, RHEED, spectroscopic ellipsometry, and XRD, collaborating with domain scientists and AI

Qualifications

• Candidates must have a PhD in Materials Science and Engineering, Computer Science, Electrical Engineering, Physics, or a related field with a strong ML research record.
• Experience in deep learning for image analysis, multimodal or real-time ML deployments is strongly preferred.
• Domain knowledge in materials science, chemistry, or physics is advantageous.

Responsibilities

• Develop ML models for autonomous thin-film materials synthesis and edge-computing deployment.
• Collaborate with LATTICE scientists and AI researchers to enable perceptual capabilities for the multi-agent workflow.
• Calibrate and validate models against diverse instrumentation data (AFM, RHEED, ellipsometry, XRD, etc.).

Skills

Applied ML

Computer vision

Scientific data analysis

Education

PhD in a related field

Tools

Python

PyTorch

JAX

Job description

Position specifics

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.

Background Checks/Clearances

Employment with the University will require successful completion of background check(s) in accordance with University policies.

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 our Benefits Page. (Note: For Postdoctoral benefits, please see our Postdoctoral Benefits page.)

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