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The Pennsylvania State University’s CEARL invites applications for a postdoctoral scholar to advance numerical analysis using machine learning-enabled multi-physics tools for predictive modeling, optimization, and Digital Twin components in Ka- and W-Band RFIC phased array antenna systems. You will work under Prof. Douglas H.
Werner in a collaborative environment with engineers and data scientists. Requirements include a PhD in electrical engineering with electromagnetics focus and expertise in
A postdoctoral scholar position is available in the Computational Electromagnetics and Antennas Research Laboratory (CEARL) in the Electrical Engineering Department at The Pennsylvania State University. This position involves the development and application of numerical analysis approaches using Machine Learning based multi-physics tools for predictive modeling, high-dimensional optimization, uncertainty quantification, forward and inverse design, including the development of Digital Twin components, under the supervision of Prof. Douglas H. Werner. The objective of this work will be to support a large multidisciplinary team effort focused on the design, modeling, and fabrication of advanced RFIC Phased Array Antenna Systems (Ka-Band and W-Band) and associated Power Converters. The postdoctoral scholar is expected to work in a highly collaborative environment in which electrical engineers, mechanical engineers, material/data scientists, and thermal engineers will collaborate to develop the next generation of microelectronics manufacturing.
Applicants must have a PhD in electrical engineering by the start date with a strong focus in electromagnetics. The candidates must have expertise in computational electromagnetics, RFIC phased array antenna design, and machine learning. This is a term appointment funded for one year from the date of hire, with additional years of funding available if there is mutual interest.
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Penn State is an equal opportunity, affirmative action employer, and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
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