The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that solve full waveform inversion problems to recovery the material properties and distribution of targets from underground radar imaging modalities such as ground penetrating radar (GPR). The project goal is to utilize both analytical and learning-based architectures to generate detailed maps of underground scenes using radar measurements acquired at the surface. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, large-scale optimization, learning-based modeling for imaging, full waveform inversion, seismic imaging, ground penetrating radar. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

MERL is seeing a motivated and qualified individual to conduct research on electric machine fault analysis and detection. Ideal candidates should be Ph.D. students with solid background and publication record in one more research area: electric machine design, analysis, fault detection, and predictive maintenance. Research experiences on modeling and analysis of electric machines and fault detection are required. Hands-on experience with permanent magnet synchronous motor (PMSM) design, data analysis, and machine learning techniques are highly desirable. Start date for this internship is flexible and the duration is 3-6 months.

MERL is seeking a motivated and qualified intern to conduct research on topology optimization of electrical machines. The ideal candidate should have solid background and demonstrated research experience in mathematical optimization methods, in particular in topology optimization, robust optimization, and sensitivity analysis. Hands-on coding experiences with the implementation of topology optimization algorithms and finite-element simulation are desirable. Knowledge and experience with electric machine principle, design and finite-element analysis is a strong plus. Senior Ph.D. students in related expertise are encouraged to apply. The start date is flexible and typical duration is about 3 months.

MERL is seeking a highly motivated intern for a research position in spacecraft attitude dynamics and control. The ideal candidate is a PhD student with experience in attitude kinematics and dynamics, multi-body dynamics, Lagrangian or Hamiltonian mechanics, optimization, and control of rigid bodies. Experience in computational fluid dynamics (CFD) using OpenFOAM, multi-phase flow modeling, and volume-of-fluid approach is desirable. Publication of results produced during the internship is expected. The duration of the internship is 3-6 months, and the start date is flexible.

We seek highly qualified candidates for research on co-design and optimization of metasurfaces and machine vision algorithms, with a particular interest in polarization. Strong candidates will have a background in metasurface optics, fluency with FDTD and RCWA simulation tools, and some familiarity with optimization methods used in computer vision and machine learning.