Postdoctoral Scholar - Reinhart Group - Materials Science and Engineering

The Pennsylvania State University; Pennsylvania; On-site; USD 48,000 - 64,000; Full time

Job summary

The Pennsylvania State University invites applications for a Postdoctoral Scholar in the Reinhart Group, Materials Science and Engineering, University Park. The role focuses on machine learning for autonomous thin-film synthesis, including representation learning, multimodal data fusion, and real-time predictive modeling within LATTICE, a NSF programmable cloud lab.

The successful candidate will develop ML models to interpret data from AFM, RHEED, ellipsometry, XRD, and similar instruments,

Qualifications

• PhD in Materials Science and Engineering, Computer Science, Electrical Engineering, Physics, or related field.
• Strong record of research in applied machine learning, computer vision, or scientific data analysis.

Responsibilities

• Develop ML models for autonomous thin-film materials synthesis.
• Deploy models on edge computing infrastructure for real-time feedback during deposition.
• Collaborate with LATTICE scientists and AI researchers to integrate models with data platforms.

Skills

Machine learning

Python

Computer vision

Edge computing

Data fusion

PyTorch

JAX

Education

PhD in MSE/CS/EE/Physics

Tools

Python

PyTorch

JAX

Job description

Postdoctoral Scholar - Reinhart Group - Materials Science and Engineering### Postdoctoral Scholar - Reinhart Group - Materials Science and EngineeringApply: Penn State University Park: Full time: Posted Yesterday: REQ\\_0000082292# **APPLICATION INSTRUCTIONS:*** ## CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process. Please do not apply here, apply internally through Workday.* ## CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday.* ## If you are NOT a current employee or student, please click “Apply” and complete the application process for external applicants. **Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants.**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. **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.