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 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 machine learning based electric machine design and optimization. Ideal candidates should be Ph.D. students with solid background and publication record in electric machine design and optimization, as well as machine learning especially for inverse design. Hands-on experiences with the implementation of optimization algorithms, machine learning and deep learning methods are required. Strong programming skills using Python/PyTorch are expected. Knowledge and experience with electric machine principle, design and finite-element analysis are highly desirable. Start date for this internship is flexible and the duration is 3-6 months.

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.

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.