Graduate Research Opportunities
Current RA Opportunities
This is NOT a complete list of RA positions. Please reach out to our graduate advisor with further inquiries. For positions listed below, students are encouraged to contact the faculty advisor listed for more detail information.
| Faculty Advisor | Expected Start Date |
|---|---|
| Yuping Zeng | Fall 2025 |
| GaN RF circuit design and simulation Qualifications: Basic understanding of device and circuit analysis. |
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| Mario Junior Mencagli | Spring 2025 |
| This project aims to develop time-modulated networks that allow going beyond the fundamental limits, such as bandwidth and reciprocity, faced by their time-invariant counterparts. This project will involve designing, fabricating, and testing. Qualifications: Strong work ethic and commitment to learn; background on electronic circuits and Matlab programming; prior exposure to Keysight ADS, PCB design, and microwave measurements. Accepting Ph.D applicants. Require citizenship: No |
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| Mario Junior Mencagli | Spring 2025 |
| This project aims to develop a new low-profile antenna solution with beam steering capability based on Metasurfaces. This project will involve designing, fabricating, and testing. Qualifications: Strong work ethic and commitment to learn; background on electromagnetic and antenna theory, and Matlab programming; prior exposure to CST Microwave Studio, Ansys HFSS, Comsol Multiphysics, and microwave measurements. Accepting Ph.D applicants. Require citizenship: No |
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| Mario Junior Mencagli | Spring 2025 |
| This project aims to develop a reconfigurable wave-based computing platform based on the concept of Metamaterials that allows multiple operations, such as matrix-vector multiplication and differentiation, to be performed with the same device. Qualifications: strong work ethic and commitment to learn; background on electromagnetic theory; prior exposure to Matlab programming, CST Microwave Studio, Comsol Multiphysics, Ansys HFSS, and microwave measurements.Accepting Ph.D applicants. Require citizenship: No |
|
| Xiang-Gen Xia | Spring 2024 |
| This project is about multidimensional remaindering and its applications in analog to digital converter and radar imaging. Qualifications: Strong math background is required. Students with math BS degree are encouraged to apply. Accepting Ph.D applicants |
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| Vishal Saxena | Spring 2024 |
| 2 positons in mmWave Integrated Circuit Design. The project involves design of receiver building blocks at 94GHz using BiCMOS process. Qualifications: The positions requires candidates with an aptitude for analog integrated circuit design and a strong work ethic and commitment to learn. Accepting Ph.D applicants |
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| Vishal Saxena | Spring 2024 |
| GaN-on-Si Power Amplifier Design Qualifications:The position requires candidates with an aptitude for analog integrated circuit design and a strong work ethic. Prior exposure to IC or board-level RF Design through coursework or project preferred. Accepting Ph.D applicants |
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| Vishal Saxena | Spring 2024 |
| Electronic and Photonic IC Design for projects in recofigurable Photonics and RF Machine Learning hardware. 2-3 Positions available. Qualifications:The position requires candidates with an aptitude for interdisciplinary analog integrated circuit design and a strong work ethic and eagerness to learn photonic IC design and Deep Learning. Accepting Ph.D applicants |
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| Yuping Zeng | Fall 2024 or later |
| Students will perform material growth, device fabrication and characterization for electron devices Qualifications: Students who have background on device fundamentals, transistor physics are welcome to apply. Accepting Ph.D applicants |
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| Yuping Zeng | Fall 2024 or later |
| Electrical and Engeneering. GaN-based Thin Flim Transistor and its RF & High Power application. Qualifications: Fabrication skills, background theory about semiconductor physics. Accepting Ph.D applicants |
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| David Hong | Spring 2024 |
| These research projects are focused on the following unsupervised machine learning topics: matrix and tensor decomposition, principal component analysis (PCA), subspace learning in the context of heterogeneous big data analysis with applications to genomics, astronomy, and imaging. Qualifications: Beyond a strong work ethic and eagerness to learn, the position requires the following preparation: linear algebra (undergraduate level is sufficient), probability (undergraduate level is sufficient), skill in programming (experience using Julia preferred but not required). Accepting Ph.D or MS applicants. |
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