Job ID: 147643

Ph.D. Openings in Machine Learning for Mechanical/Civil/Aerospace Engineering and Mechanics

Michigan Technological University

  • Oct. 13, 2020
  • Ph.D. Openings in Machine Learning for Mechanical/Civil/Aerospace Engineering and Mechanics
  • Mechanical Engineering - Engineering Mechanics
  • Michigan Technological University
    Houghton, MI
  • Review starts immediately and open until filled
  • Spring or Fall 2021
  • Graduate Student
  • Transportation Engineering
    Structural Engineering
    Ocean Engineering
    Naval Architecture & Marine Engineering
    Mechanical Engineering
    Manufacturing & Quality Engineering
    Industrial & Systems Engineering
    Engineering Physics
    Engineering Mechanics
    Energy Technology
    Civil Engineering
    Engineering - Other

Research assistantships (full financial support) are available immediately for prospective Ph.D. students at Dr. Yongchao Yang’s group in the Mechanical Engineering Department at Michigan Tech. Dr. Yang is actively seeking motivated Ph.D. students, who will conduct their thesis research in developing physics-aware machine/deep learning methodology to advance the broad cyber-physical systems and artificial intelligence (AI) technology and applications.

The Ph.D. students’ specific directions are flexible and can exploit one or a couple among a wide class of structural/system dynamic models (e.g., vibrational, thermal, elastic waves, multi-scale dynamics, etc). They will explore exciting state-of-the-art techniques such as machine/deep learning, signal/image processing, optimization, networks, etc for topics in broad 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 in Mechanical/ Aerospace/Civil/Electrical fields.

The students will join a multi-disciplinary research team of structural/mechanical/computer faculty and researchers and will also collaborate with Argonne National Laboratory ( and Los Alamos National Laboratory (

The positions are available immediately (Spring or Fall 2021). Interested students are welcome to directly contact Prof. Yongchao Yang ( with CV and research interests/statements.

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 on his webpage (

  • As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.

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Contact Information

  • Yongchao Yang
    Department of Mechanical Engineering - Engineering Mechanics
    Michigan Tech
    1400 Townsend Drive
    Houghton, MI 49931
  • 906-487-3405

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