The Control and Dynamical Systems (CD) group at MERL is seeking highly motivated interns at different levels of expertise to conduct research on planning and control for autonomous vehicles. The research domain includes algorithms for path planning, vehicle control, high level decision making, sensor-based navigation, driver-vehicle interaction. PhD students will be considered for algorithm development and analysis, and property proving. Master students will be considered for development and implementation in a scaled robotic test bench for autonomous vehicles. For algorithm development and analysis it is highly desirable to have deep background in one or more among: sampling-based planning methods, particle filtering, model predictive control, reachability methods, formal methods and abstractions of dynamical systems, and experience with their implementation in Matlab/Python/C++. For algorithm implementation, it is required to have working knowledge of Matlab, C++, and ROS, and it is a plus to have background in some of the above mentioned methods. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.

The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop computational imaging algorithms for a variety of sensing applications. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, learning for inverse problems, large-scale optimization, blind inverse scattering, radar/lidar/sonar imaging, or wave-based inversion. Experience with experimentally measured data is desirable. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

MERL is seeking a motivated intern to assist in generating simulated multimodal data for machine learning applications. The project involves integrating several existing software components to generate optical and radar data in a variety of sensing scenarios, and executing the simulations under a variety of conditions. The ideal candidate should have experience with C++, Python, and scripting methods. Some knowledge or experience with Blender, computer graphics, and computer vision would be preferred, but is not required. Project duration is flexible in the range of 1-2 months. Intern has the choice of part-time or full-time occupation and may start immediately.

MERL is looking for a highly motivated, and qualified individual to join our internship program of advanced phased array research. The ideal candidate should be a senior Ph.D. student with rich experience in beam forming technologies. Knowledge of wireless communication, transceiver architecture, and digital signal processing, FPGA and/or Matlab programming skills are required. RF circuits knowledge will be a plus. Duration is 3-6 months with a flexible start date.