Openings

Ph.D. & Postdoctoral Positions Available

Several PhD positions in Mechanical Engineering are available in Spring and Fall 2021 and post-doc positions are immediately available in the research group of Dr. Yi Wang at the University of South Carolina-USC (Columbia/Main campus). USC is the flagship university in the State of South Carolina, and the Ph.D. program at the department of Mechanical Engineering is ranked No. 31 nationally by the National Research Council (NRC), and the College of Engineering and Computing is ranked No. 1 in the State of South Carolina for faculty research productivity.
The group of Dr. Wang focuses on computational and data-enabled science and engineering (CDS&E) and its applications in real-world multiphysics systems, including micro/nanofluidics, energy management, and additive manufacturing. CDS&E, recently emerging as a focal point of multidisciplinary research has been applied to essentially each phase of technology development and industrial engineering, from conceptualization, virtual prototyping and design, and automation and control, to final verification and validation (V&V). Our group aims to discover and develop new methodologies, framework, and capabilities to bridge CDS&E and system engineering in the real world and with particular emphasis on multiphysics and engineering intelligence.
We are looking for highly motivated applicants in applied mathematics, mechanical engineering, electrical engineering, or chemical engineering with strong background and experience in numerical modeling and high-performance computing (CFD and FEM), machine learning, data mining, and system control in fluid dynamics, energy and additive manufacturing systems, microfluidic and nanofluidic systems, etc.
To apply, please send your CV/Resume, publications, etc. in a single PDF (for Ph.D. applicants, transcripts, and GRE scores are also required) to Dr. Wang (yiwang@cec.sc.edu) with the email subject “Position Application”.

Modeling & Computation & Control

1. Reduced Order Modeling, Machine Learning, and Design Optimization for Multiphysics Engineering Systems
We will investigate and develop reduced order modeling, machine learning, and design optimization methods for multiphysics systems for a variety of engineering applications, which include but not limited to thermal-fluidics, aerospace and aeroservoelasticity, energy materials and management, additive manufacturing, and microfluidics & nanofluidics.
2. Real-time Computing and Control on Edge Computing
We will investigate and develop real-time control framework, algorithms, and cyberphysical systems for a variety of engineering applications on edge computing platforms, which include but not limited to energy auditing & cybersecurity, multi-fidelity model optimization, and autonomous systems (real-time fault detection & mitigation, path planning, and control).
Research efforts will include at least one of the followings:
•Development of reduced order models for multiphysics engineering systems
•Development of data mining, machine learning, and optimization algorithms
•Development of CPU+GPU computing algorithms
•Development of control framework for various robotic platforms
At least one of the qualifications below is preferred:
•Strong background in control theory, linear algebra, and computational mathematics and/or mechanics
•Experience in developing numerical models, codes, and computation algorithms (CFD and FEM)
•Hands-on experience with computing in Matlab, C/C++, Python, or other object-oriented programming languages
•Hands-on experience with embedded system and edge-computing modules, such as Jetson TX series, Intel NUC, Raspberry Pi, and Arduino.
•Strong interest and self-motivation to perform cutting-edge research and conquer challenges in real-world engineering and to publish high-impact papers