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Job Record #18001
TitlePh.D. and MS (thesis) positions at Penn State Aerospace
CategoryPhD Studentship
EmployerThe Pennsylvania State University
LocationUnited States, PA, State College
InternationalYes, international applications are welcome
Closure DateThursday, December 15, 2022
There are several graduate research assistantship (GRA) positions available for Ph.D. and MS (thesis) students in the computational complex engineered systems design lab (CSDL) at Penn State. Students will have the unique opportunity to work at the intersection of Aerospace Engineering and Computational Science and will have affiliations with both the aerospace engineering department as well as the institute of computational and data sciences (ICDS) at Penn State. Students will have the opportunity to use Penn State's, state-of-the-art, Roar supercomputer. The CSDL at Penn State is interested in developing scalable computational techniques toward the design of complex systems, e.g., aircraft, turbomachinery, missiles, re-entry vehicles, and wind turbines. Some active projects include • Model order reduction: we will develop principled reduced order models for cheap emulation of nonlinear aerodynamic flows. • Multifidelity modeling, optimization, and uncertainty quantification: we will develop compu- tational methods that can exploit availability of models that trade computational cost for predictive accuracy (fidelity). This includes surrogate modeling, quantifying uncertainties at the system level, and improving tractability in high-dimensional optimization. • Artificial intelligence and machine learning (AI/ML) for design: we will develop methods rooted in AI/ML for tractable design space exploration. Examples include deep learning and manifold learn- ing for aerodynamic design optimization. • Aerospace certification by analysis (CbA): we will develop computational methods to quantify reliability and uncertainty of aerospace systems that could potentially be used for aircraft certification and qualification, by entities such as NASA and the FAA. • High-fidelity multidisciplinary design optimization (MDO): we will develop methods for optimiz- ing coupled systems (e.g., aerostructural, aero-propulsive-strcutural), in the presence of uncertainty and absence of derivative information. • Design of novel aircraft: In this application focused research, we are interested in the design and analysis of revolutionary, unconventional aircraft configurations such as the transonic truss-braced wing (TTBW) and the blended wing-bowdy (BWB). Furthermore, we are interested in studying in- tegration issues associated with novel technologies, such as flexible lightweight structures, hydrogen fuel storage/onboard generation, etc, using high-fidelity computational fluid dynamics (CFD). Qualifications. Students must have a passion to solve open challenges in aerospace related to sustain- ability, safety, and cost. Students must have demonstrated high standards in technical writing. Strong background in applied mathematics and programming (for scientific computing) is desirable. Experience with Python-based ML libraries (e.g., PyTorch) is a bonus but not required. Interested students may con- tact the principal investigator via email with a copy of their latest CV, a writing sample, and transcripts to discuss possibilities. Underrepresented minorities, first-gen graduate students, and women are particularly encouraged to apply.
Contact Information:
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NameAshwin Renganathan
Email ApplicationYes
Record Data:
Last Modified17:25:35, Wednesday, September 07, 2022

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