MEMS Graduate Student Research Assistant (RA): MS or PhD
Digital Twin and Physics Based Modeling with AI / ML Fusion for Sensor Network Optimization
The Department of Mechanical Engineering and Materials Science (MEMS) at the University of Pittsburgh invites applications for a Graduate Student Research Assistant (RA) at the MS or PhD level, with an emphasis on digital twin models and physics-based modeling coupled with artificial intelligence and machine learning techniques applied to sensor network optimization as it relates to infrastructure monitoring applications. More specifically, the student will develop digital twin and finite element-based models of selected critical infrastructures which can accurately predict measurable signatures of various operational conditions, including both normal and faulty operational states. Digital twin models will be combined with simulation results and experiments for the purpose of training artificial intelligence and machine learning frameworks that can be coupled with advanced sensing systems including distributed fiber optic acoustic sensing and non-destructive evaluation. Based upon developed models and analytical frameworks, new capabilities will also be developed for sensor network optimization based upon targeted infrastructure monitoring objectives and constraints. The candidate will have an opportunity to work closely with DOE national laboratory, industry, and other academic collaborators in development and application of the modeling and experimental interrogation framework combined with digital twin models in part through the University of Pittsburgh Infrastructure Sensing Collaboration.
Desired Interests and Qualifications:
Successful applicants should have interest in digital twin modeling, advanced data analytics methods and techniques, and applications of advanced sensor technologies such as distributed fiber optic sensors and non-destructive evaluation methods. Applicants should have a BS or MS degree in mechanical engineering, applied physics, electrical engineering, or related field. Candidates with experience in finite element analysis, machine learning, and / or acoustics are preferred.
Anticipated Start Date:
May 1st, 2024 (Summer 2024 Semester)
Interested candidates should contact Prof. Paul Ohodnicki with an updated resume at firstname.lastname@example.org and also submit an application to the mechanical engineering graduate program at their soonest opportunity.