The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that solve inverse problems in computational sensing that incorporate deep learning architectures for a variety of sensing applications. The project goal is to improve the performance and develop an analysis of algorithms used for inverse problems by incorporating new tools from machine learning and artificial intelligence. 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, large-scale optimization, plug-and-play priors, learning-based modeling for imaging, learning theory for computational imaging, and Koopman theory/dynamic mode decomposition. 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 seeking a motivated and qualified individual to conduct research in modeling, simulation and control of aircraft electrical power system. The ideal candidate should have solid backgrounds in dynamic modeling and simulation of power electronics and electrical machine, and transient analysis of overall electrical power system. Demonstrated experience in physical modeling and simulation software/language such as Modelica or Simscape is a necessity. Knowledge of aircraft dynamics and aerodynamics is a big plus. Senior Ph.D. students in aerospace, electrical engineering, control are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in extended object tracking (EOT) using automotive radar sensors. Previous experience in multiple (point and extended) object tracking, data association, and motion/measurement model learning on open automotive datasets is highly preferred. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, data analysis (Python & MATLAB), 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. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.