Remote Film Professor Jobs (NOW HIRING)

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• 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, seeNotice to Out of State Applicants.

This is a term position; length of the term will be discussed during the interview process. Continuation past the termlengthdiscussed willbebasedonuniversityneed,performance,and/oravailabilityoffunding.

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 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.

Candidates must have a Ph.D. in Materials Science and Engineering, Computer Science, Electrical Engineering, Physics, or a related field, and should have postdoctoral or equivalent research experience demonstrating an independent record of publication in applied machine learning, computer vision, and/or scientific data analysis. 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.

This is a non-tenure-line, limited-term appointment funded for one year from the date of hire with excellent possibility of renewal.

To apply, applicants should upload a cover letter detailing their qualifications for and interest in the position, a copy of their CV, and names and contact details (e-mail addresses and telephone numbers) for three references. Review of applications will begin immediately and continue until the position is filled.

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 wellbeing. In addition to comprehensive medical, dental, and vision coverage, employees enjoy robust retirement plans. One of the standout benefits is the generous 75% tuition discount, available to employees as well as eligible spouses and children.

For more detailed information, please visit ourBenefits Page. (Note: For Postdoctoral benefits, please see our Postdoctoral Benefits page.)

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