MERL is seeking a highly motivated candidate for development of next-generation high-precision positioning methods for autonomous systems applications, e.g., autonomous driving. The candidate will work with the Control for Autonomy team and the Signal Processing group in developing satellite-based positioning methods using information from multiple sources. Previous experience with at least some of the Bayesian inference, distributed estimation, satellite navigation systems, is highly desirable. Solid knowledge in MATLAB is required, working experience in C/C++ is desired, and previous experience with satellite navigation packages such as RTKLib is a merit. PhD candidates meeting the above requirements are encouraged to apply. The expected duration of the internship is 3-6 months with flexible start date. 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 and qualified intern to collaborate with the Control for Autonomy team in the development of algorithms for planning and control of autonomous vehicles. The potential subjects include high level decision making using formal methods and set-based control, coordination or perception and control strategies to improve environment knowledge while achieving a goal, and distributed control for multi-vehicle systems. The ideal candidate is expected to be working towards a PhD with strong emphasis in control or planning algorithms, and to have interest and background in as many as possible among: motion planning, predictive control, perception and object detection optimization, machine learning for vehicle prediction, autonomous vehicles. Good programming skills in MATLAB, Python or C/C++ are required. The expected duration of the internship is in the Spring of 2021, for a duration of 3-6 months. 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 looking for a self-motivated and qualified candidate to work on air flow control of Heating, Ventilation and Air Conditioning (HVAC) systems. The ideal candidate is a PhD student and should have experience and records in multiple of the following areas: fluid dynamics, control theory, reinforcement learning, familiarity with partial differential equations. Proficiency in Python and Matlab is required. The successful candidate will be expected to develop, in collaboration with MERL employees, a state of the art algorithms for air flow control that will lead to a scientific publication. Typical internship length is 3 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.