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美国南卡罗来纳大学王懿教授招收博士、博士后公告

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 楼主| 发表于 2017-8-25 20:41:38 | 显示全部楼层 |阅读模式
Multiple 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://yiwangusc.wixsite.com/yiwang, Dr. Wang will take the position in August, 2017). 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].
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 a particular emphasis on multiphysics and engineering intelligence.
We are looking for highly motivated applicants in mechanical engineering, aerospace engineering, electrical engineering, or chemical engineering with strong background and experience in numerical computation 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 (for Ph.D. applicants, transcripts, and GRE scores are also required) to Dr. Wang (yi.wang16@yahoo.com) with the email subject “Position Application. Detailed descriptions for the positions are:

(1) Real-time Simulation for Process Control and Design of Multiphysics Systems
We will develop methodologies and frameworks of computationally efficient physics-based models and data-driven models to speed up process simulation and establish compact database in high-dimensional parameter space for online system control, prognostic health management, and design optimization. The multiphysics systems in study target a variety of real-world applications in industry, including additive manufacturing, energy systems and battery, aeroservoelasticity, microfluidics/nanfluidics, etc.
Research efforts will include
· Formulation and development of high-fidelity computational models (Finite volume method/VM, Finite element method/FEM) and varying-fidelity models
· Development of data-driven surrogate models and reduced order models
· Development of data mining and machine learning algorithms and its applications to practical engineering systems
The required and desired qualifications include:
· Computational background and experience in one (or several) of the following systems is required: additive manufacturing, microfluidics/nanofluidics, thermal management, energy and battery systems, aerodynamics and aeroservoelasticity (fluid-structure-interaction).
· Familiarity with at least one of the common CFD/FEM tools is required, e.g., Comsol, OpenFOAM, CFD-ACE+, STAR-CCM+, FUN3D, Loci-CHEM, etc.
· Strong background in numerical algebra, optimization, linear dynamic systems, transport phenomena is required
· Strong skills of developing (and compiling) highly parallelizable computing solvers/models using OpenMP and MPI within Matlab, C/C++, Python, or other object oriented programming languages
· Experience with reduced order modeling, data-driven modeling, computer vision, and virtual reality 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
(2) Data Analytics and Management for Multiphysics Engineering Systems
We will investigate and develop analytics and management frameworks for both real-time data streams and large-scale high-fidelity simulation data to discover key physics/patterns and build predictive models for a variety of engineering applications, which include but not limited to target identification and discrimination, design of experiments, online simulation and prediction, test planning and optimization, among others.
Research efforts will include
· Development of data-driven surrogate models and system identification capabilities for multiphysics engineering systems
· Investigation of approaches of Kalman filter family and model/data fusion for real-time prediction
· Development of data mining and machine learning algorithms, in particular, data reduction/compression, supervised and unsupervised learning
· Uncertainty quantification and inverse engineering problems
· Energy-efficient computing
The required and desired qualifications include:
· Computational background and experience in one (or several) of the following systems is required: aerodynamics and aeroservoelasticity, additive manufacturing, microfluidics/nanofluidics, thermal-fluidic systems, energy and battery management.
· Strong hands-on experience with highly parallelizable, numerical computing for data analytics within Matlab, C/C++, Python, or other object oriented programming languages
· Experience with GPU-based computing, uncertainty quantification, and Bayesian inference 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
(3) Autonoumous Microfluidic/Nanofluidic Systems  (Cyber-Micro/Nanofluidic Systems)
We will investigate intelligent design methodologies and develop cyber-physical frameworks to integrate modeling , data analytics and design, networks, and control for autonomous microfluidic/nanofluidic systems with the aim to improve system performance while minimizing adverse footprints to environment. The microfluidic/nanofluidic systems in study will include micro/nano electrokinetics (DEP-based and concentration polarization-based sample manipulation), controlled species transport for biochemical assays (e.g., concentration gradient generation), and high-throughput cell sorting (inertial microfluidics, deterministic lateral displacement, etc.)
Research efforts will include
· Development of computationally efficient models for system-level simulation of microfluidic and nanofluidic systems
· Multidisciplinary design analysis and optimization of microfluidic and nanofluidic devices
· “Intelligent Design” based on machine learning and data mining
· Microfluidic/nanofludic system automation and control
· Uncertainty quantification and system inference
The required and desired qualifications include:
· Experience with Finite Volume Method (FVM) and/or Finite Element Method (FEM) modeling and solver development in one (or several) of the following physics is required: fluid dynamics and electrokinetics, heat transfer, species transport and reaction kinetics, particle dynamics, etc.
· Experience with at least one of the common CFD/FEM tools is required, e.g., Comsol, OpenFOAM, CFD-ACE+, STAR-CCM+, and others
· Strong background in numerical algebra, optimization, and control theory required
· Previous work experience in one or more of the following application areas is a significant plus: Biomedical devices, microfluidic system instrumentation (fluidic control, fluorescent microscopy), experimentation on cell-based bioassays (chemotaxis, cell sorting and assembly, etc.).
· 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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