Ph.D Position available at University of South Carolina(CSCE related)

PhD in Mechanical Engineering is available in the research group of Dr. Yi Wang at the University of South Carolina(https://www.sc.edu/study/college ... y-staff/yi_wang.php).
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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, additive manufacturing, aerodynamics & aerospace. 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, aerospace 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 aerospace, energy and additive manufacturing systems, microfluidic and nanofluidic systems, etc. To apply, please send your CV/Resume, publications, transcripts, Toefl, GRE etc. in a single PDF to "yiwang@cec.sc.edu".

Position Description:
Numerical Modeling and Machine Learning for Multiphysics Engineering Systems Design
We will investigate and develop numerical modeling and machine learning methodology and frameworks for predictive analysis and design of multiphysics systems for a variety of engineering applications, which include but not limited to microfluidics & nanofluidics, photonic integrated circuits (PIC), energy management, and additive manufacturing.

Research efforts will include
•    Development of data-driven and physics-based models for multiphysics engineering systems
•    Development of data mining and machine learning algorithms, in particular, data reduction/compression, supervised and unsupervised learning, and deep neural network (DNN)
•    Uncertainty quantification and design optimization

The required qualifications include:
•    Strong background in numerical algebra, optimization, and control theory
•    Experience in developing in-house numerical models, codes, and computation algorithms for various linear and nonlinear dynamical systems.

The desired qualifications include:
•    Strong hands-on experience with parallel computing and optimization for numerical models, data analytics, and machine learning within Matlab, C/C++, Python, or other object-oriented programming languages
•    Numerical modeling experience in one (or several) of the following systems: microfluidics & nanofluidics, thermal-fluidic systems, photonic integrated circuit, energy and battery management.
•    Experience with GPU-based computing and/or heterogeneous computing for numerical computation and deep-learning is a significant plus
•    Strong interest and self-motivation to perform cutting-edge research and conquer challenges in real-world engineering and to publish high-impact papers

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