Assistant Research Professor

Penn State University•University Park, IL

3d•Onsite

About The Position

The Materials Research Institute (MRI) at The Pennsylvania State University invites applications for a non-tenure-line Assistant Research Professor. 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 successful candidate will lead the development of 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 candidate 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. The candidate will also contribute to the scientific direction of the LATTICE ML/AI effort and mentor graduate students and postdoctoral scholars as appropriate.

Requirements

Ph.D. in Materials Science and Engineering, Computer Science, Electrical Engineering, Physics, or a related field.
Postdoctoral or equivalent research experience demonstrating an independent record of publication in applied machine learning, computer vision, and/or 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

Lead the development of specialized machine learning models that enable AI systems to interpret scientific data from instruments.
Deploy these models on edge-computing infrastructure for real-time feedback during deposition.
Integrate models with the Lifetime Sample Tracking data platform.
Collaborate closely with LATTICE 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.
Contribute to the scientific direction of the LATTICE ML/AI effort.
Mentor graduate students and postdoctoral scholars as appropriate.