Moore Dynamics & Analytics Lab (Ph.D. Research Assistant)

Moore Dynamics & Analytics Lab (Ph.D. Research Assistant)
Contact - Keegan Moore
Department - Civil & Environmental Engineering - Mechanical & Materials Engineering
Students Needed:
• Graduate

Position Description

The Moore Dynamics & Analytics Laboratory (MoDAL) led by Prof. Moore in Mechanical and Materials Engineering at the University of Nebraska-Lincoln is seeking a talented student for a Ph.D. position starting in Summer/Fall 2022. The position is funded by the US Air Force and provides a competitive stipend, tuition, and benefits.

The project focuses on automating the process of validating and updating computational models using experimental measurements. The research also tackles the automation of processing of raw measurements into forms suitable for model validation and updating. The student will be responsible for deriving new data-driven algorithms; creating computational models for beams, wings, and model aircraft; and designing experimental systems and performing the corresponding measurements.

Position Requirements

Prospective students must have a B.S. or M.S. in Mechanical Engineering, Aerospace Engineering, Civil Engineering, Applied Mathematics, Physics, or other related fields. Other majors can also be considered if the applicant has a strong interest or motivation in digital engineering and machine learning/AI as applied to understanding test and model results of engineering systems. US Citizenship is required.

Application Instructions

Interested students should send a copy of their CV and a statement explaining their interest in this research to Prof. Moore by email at kmoore@unl.edu. Candidates will also need to apply for official admission to the Doctor of Philosophy in Mechanical Engineering and Applied Mechanics Program. Please indicate your interest in this opportunity by listing it in your statement of purpose and research interests.

Research Abstract

This research focuses on data-driven and deep learning approaches for autonomizing the validation and updating of digital models using Test and Evaluation (T&E) data. The first part of this research will create novel overlapping neural networks that leverage the principle of time reversibility to autonomously repair T&E data with missing data segments. The second portion will produce advanced mathematical techniques for infusing physics into autoencoder neural networks for extracting corresponding universal representations from both test and model results, facilitating the comparison of similar but disparate datasets. The third part will introduce and deploy new generator-discriminator-translator networks by leveraging the power of generative adversarial networks to autonomously update digital model parameters using T&E data. The new deep learning frameworks will be employed on data taken from computergenerated signals, numerical simulations, and experimental measurements.