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Post-doc positions for Fluid-Structure Interaction Modeling

Location
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, ****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] ****[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: Reduced Order Modeling and Data Analytics for Fluid Structure Interaction and Aeroservoelasticity We will investigate and develop parametric reduced order modeling (ROM), data-driven modeling, and machine learning methodology and frameworks for predictive analysis and design/control of fluid-structure interaction (FSI), aeroelasticity (AE), and aeroservoelasticity (ASE) for a variety of aerospace applications. Research efforts will include • Development of both data-driven models and physics-based models for aeroservoelasticity across varying Mach regimes • Development of data mining and learning algorithms, and deep neural network (DNN) for aerostructural design and optimization The required qualifications include: • Experience with developing inviscid and Navier-Stokes solvers for compressive flow (preferentially high-speed flow) using finite volume approaches on structured and unstructured grid • Experience with FSI development within the OpenFOAM and SU2 environment and strong hands-on experience with parallel computing • Experience with structural dynamics analysis • Proficiency in C , Fortran, and/or Python The desired qualifications include: • Experience with reduced order modeling (ROM), aerostructural optimization, data analytics, and machine learning • Experience for panel methods and Nastran tools of aeroelasticity is a significant plus. • Experience with aerostructural control and flight control (6 DOF) is a plus • Hands-on experience using Star-CCM for aeroelasticity/aeroservoelasticity • 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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