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.

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.