MERL is looking for a highly motivated individual to work on safe control of data-driven, uncertain, dynamical systems. The research will develop novel optimization and learning-based control algorithms to guarantee safety in various industrial applications, including autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: optimal control under uncertainty, (convex and non-convex) optimization, and (reinforcement and statistical) learning. Ph.D. students in engineering or mathematics with a focus on control, optimization, and learning are encouraged to apply. A successful internship will result in the submission of relevant results to peer-reviewed conference proceedings and journals, and the development of well-documented (Python/MATLAB) code for MERL. The expected duration of the internship is 3-6 months, and the start date is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

MERL is seeking a highly motivated intern to research multi-agent motion planning by combining optimization-based methods with machine learning. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in multi-agent motion planning, machine learning (especially supervised, reinforcement, and safe ML), and convex and non-convex optimization. A successful internship will result in innovative methods for multiagent planning, in the development of well-documented (Python/MATLAB) code for validating the proposed methods, and in the submission of relevant results for publication in peer-reviewed conference proceedings and journals. The expected duration of the internship is 3 months with a flexible start date in the Spring/Summer 2021. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

MERL is seeking highly motivated interns for research positions in guidance, navigation, and control of spacecraft. The ideal candidates have experience in one or more of the following topics: astrodynamics, the three-body problem, relative motion dynamics, rendezvous, attitude control, orbit control, orbit determination, nonlinear estimation, computer vision, and optimization-based control. PhD students in aerospace, mechanical, or electrical engineering are encouraged to apply. Publication of results produced during the internship is expected. The duration of the internships are 3-6 months, and the start dates are flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.