The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in mutual interference mitigation for automotive radar. Previous experience in waveform design, radar detection under interference, joint communication and sensing, interference mitigation, and deep learning for radar is highly preferred. Knowledge about automotive radar schemes (MIMO and waveform modulation, e.g., FMCW, PMCW, and OFDM) is a plus. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments using MERL in-house testbed, and prepare results for patents and publication. Senior Ph.D. students with research focuses on signal processing, machine learning, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date.

The Computational Sensing team at MERL is seeking motivated and qualified individuals to assist in the development of computational methods 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, sensing of dynamical systems, 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. 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 highly motivated intern to conduct research on data-driven energy management strategies for (hybrid) electric vehicles. The candidate will develop methods that use data, e.g., of human drivers or traffic conditions, in order to improve the control of electric vehicles. The ideal candidate will have experience in either one or multiple of the following topics: model predictive control, machine learning, statistical learning, numerical optimization, and (inverse) optimal control. Prior experience with (hybrid) electric vehicles is a plus. Good programming skills in MATLAB, Python, or C/C++ are required. PhD students in engineering or mathematics with a focus on control theory or numerical optimization are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. The expected duration of the internship is 3-6 months. 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 a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of hybrid control algorithms for cyberphysical system. The potential subjects include formal methods for control synthesis, control barrier-functions, stabilizing control for hybrid dynamical systems, and optimal control of hybrid dynamics. The ideal candidate is expected to be working towards a PhD with strong emphasis in control theory, and to have interest and background in as many as possible among: predictive control, Lyapunov stability, formal methods for control, constrained control, optimization, and machine learning. Good programming skills in MATLAB, and/or Python 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.