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Post-doc positions for Modeling and Data Analytics

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Columbia, SC

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Post-doc positions in Mechanical Engineering are immediately available in the research group of Dr. Yi Wang at the **MEMBERS ONLY**SIGN UP NOW***.-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) [1], and the College of Engineering and Computing is ranked No. 1 in the State of South Carolina for faculty research productivity [2]. [1] ****[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 aerodynamics & aerospace, micro/nanofluidics, energy management, additive manufacturing. 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, or chemical engineering with strong background and experience in numerical modeling and high-performance computing, machine learning, data mining, and system control in aerospace, energy and additive manufacturing, and microfluidic and nanofluidic systems, etc. To apply, please send your CV/Resume, publications, etc. in a single PDF to Dr. Wang (****) with the email subject “Position Application”. • Post-doc applications: please indicate your current visa status (if available) Detailed description for the position: Numerical Modeling and Data Analytics for Multiphysics Engineering Systems We will investigate and develop numerical modeling, data-driven 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 aerodynamics, micro/nanofluidics, photonic integrated circuits (PIC), energy management, and additive manufacturing. Research efforts will include • Development of data-driven models and physics-based models for multiphysics engineering systems (aerospace, micro/nanofluidics, photonic integrated circuit, etc.) • Development of data mining and machine learning algorithms, in particular, data reduction/compression, supervised and unsupervised learning, and deep neural network (DNN) • Design optimization • Sensitivity analysis and uncertainty quantification The required qualifications include: • Strong background in numerical computation, advanced linear 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: aerodynamics, microfluidics & nanofluidics, 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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