MERL is seeking a motivated and qualified individual to conduct research in modeling, control, simulation and analysis of electric system involved in xEV and electric aircraft. The ideal candidate should have solid backgrounds in some of the following areas: modeling, control, and simulation of electrical systems (including generators, motors, power electronics and batteries), aerodynamics, mission analysis, flight dynamics, and multi-disciplinary system design optimization. Demonstrated experience in software/language such as Modelica or Matlab/Simulink/Simscape is a necessity. Knowledge and experience of CarSim, NPSS, SUAVE, and FLOPS is a definite plus. Senior Ph.D. students in automotive, aerospace, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

MERL is looking for a highly motivated individual to develop planning and control algorithms for multi-agent systems for resource monitoring. The ideal candidate has experience in multi-agent motion planning and data-driven sequential decision-making. The ideal candidate will have published in one or more of these topics: planning over discrete spaces, task scheduling and assignment, vehicle routing and scheduling problems, multi-arm bandits, reinforcement learning, and planning and control of aerial and ground robots. The candidate should have a working knowledge of ROS and Python/C++ since the internship will include validation in various simulation/hardware testbeds at MERL. The minimum duration of the internship is 3 months; the start time is Summer/Fall 2023.

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.