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 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
Summary from the source posting. Always confirm details on the institution's official career page.