The successful candidate for this position will perform research to develop new approaches to advance modeling and characterization of physical systems and engineering structures/materials. The work involves development of new algorithms for dynamical system modeling, identification, and characterization through integration of machine/deep learning and physics models. In particular, the successful candidate has the flexibility of exploiting one or a couple among a wide class of structural/system dynamic models (e.g., vibrational, thermal, elastic waves, multi-scale dynamics, etc). The selected candidate will join a multi-disciplinary research team of structural/mechanical/computer researchers to develop machine/deep learning based algorithms for broad application areas of cyber-physical systems and infrastructure resiliency such as structural dynamics sensing and identification, multi-scale material characterization and detection, and structural health monitoring and non-destructive evaluations.
The successful candidate will also collaborate with scientists at Argonne National Laboratory (https:www.anl.gov) and Los Alamos National Laboratory (https://www.lanl.gov/).
- Background and research experience in Mechanical/Civil/Electrical/Aerospace/Industrial Engineering, Computer Science, Statistics, Applied Mathematics, or related fields.
- Experience in machine/deep learning, signal/image processing, optimization, networks, or other related expertise.
- Demonstrated experience in developing/applying techniques (e.g., acoustics/ultrasonics, thermography, optics, etc, or other related domains/tools) for nondestructive evaluations, structural health monitoring, additive manufacturing, or other related engineering or mechanics-related applications.
- Programming experience in Python or C/C++, or related. Experience in Keras/Tensorflow, or other related platforms.
- Good communication skills both verbal and written.
The position is available immediately and review of the applications will start immediately. Candidates with outstanding qualifications will be considered for the rank of Research Scientist. For applications for this position or any questions, please directly contact Prof. Yongchao Yang (email@example.com).
About the PI – Dr. Yongchao Yang is an Assistant Professor in the Department of Mechanical Engineering – Engineering Mechanics at Michigan Tech. He was a Staff Scientist at Argonne National Laboratory (2018-2019), after a Director’s Funded Postdoctoral Fellowship at Los Alamos National Laboratory (2015-2017). He obtained his Ph.D. from Rice University in 2014 and bachelor’s from Harbin Institute of Technology in 2010, both in structural engineering. His expertise is in structural dynamics, experimental mechanics, system identification and health monitoring. His recent research, funded by DARPA and DOE, has focused on developing new high-resolution structural sensing/imaging and identification methods, combining approaches from computer vision and machine learning. He is the author of more than 30 international journal publications, 3 book chapters, and 2 US patents.
Dr. Yang has received a number of awards and recognitions. He was a recipient of the Best Paper Award of the United Nations International Conference on Sustainable Development (New York, 2015), a winner of the TechCrunch Disrupt NY (New York, 2016), mentored a student winning a 2nd place in the student competition of the IEEE Resilience Week (Chicago, 2016), and received the Mary & Richard Mah Publication Prize for Engineering Science (2018), the 2017 Raymond C. Reese Research Prize of American Society of Civil Engineers (ASCE), and the latest R & D 100 Award (2018).
Find out more about Dr. Yang’s recent publications and sponsored projects in his webpage (https://www.mtu.edu/mechanical/people/faculty/yang-y/).