p***8
发帖数: 36 | 1 PhD and post-doc positions in Mechanical Engineering are immediately
available in the research group of Dr. Yi Wang at the University of South
Carolina-USC (Columbia/Main campus, https://www.sc.edu/study/colleges_
schools/engineering_and_computing/faculty-staff/yi_wang.php). 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) [1], and the College of Engineering and
Computing is ranked No. 1 in the State of South Carolina for faculty
research productivity [2].
[1] http://www.me.sc.edu/about/
[2] https://sc.edu/study/colleges_schools/engineering_and_computing/about/
employment/
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. 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 math, 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, etc. in a single PDF to Dr. Wang ([email protected])
with the email subject “Position Application”.
• Ph.D. applicants: please also send your transcripts, and GRE
scores
• Post-doc applications: please also indicate your current visa
status (if available)
Detailed description for the position is:
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 required
• 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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