Optimization

MERL is seeking a motivated and qualified intern to conduct research on machine learning based electric motor design and optimization. Ideal candidates should be Ph.D. students with a solid background and publication record in electric machine design, optimization, and machine learning. Hands-on experience with the implementation of optimization algorithms, machine learning and deep learning methods is 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 about 3 months.

MERL is looking for a highly motivated and qualified candidate to work on stochastic control for grid-interactive net-zero energy buildings informed by deep generative models. The ideal candidate will have a strong understanding of optimization-based control with expertise demonstrated via, e.g., publications, in stochastic model predictive control.

Additional understanding of energy systems and machine learning is a plus. Hands-on programming experience with numerical optimization solvers and Python fluency is required. The results of this 3-6 month internship are expected to be published in top-tier energy systems and/or control venues.

MERL is seeking a motivated and qualified individual to work on data-driven estimation and control of high-dimensional dynamical systems, with applications in indoor airflow optimization. The ideal candidate will be a PhD student in engineering or related fields with a solid background in estimation, control, and dynamical systems theory. Preferred skills include knowledge of reinforcement learning, data-driven control, nonlinear control, reduced-order modeling (ROM), and partial differential equations (PDEs). Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.