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.

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.

The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in automotive radar-based object tracking and perception for autonomous driving. Previous experience on multiple (point and extended) object tracking, data association, and data-driven object detection/tracking is highly preferred. Knowledge about automotive radar schemes (MIMO array and waveform modulation (FMCW, PMCW, and OFDM)) and hands-on experience on open automotive datasets are a plus. Knowledge on vehicle dynamics is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, conduct field measurements, 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.

MERL is looking for a self-motivated intern to work on electrical machine modelling and signal processing. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in electrical machines, signal processing, and electrical circuit analysis. Experience in transient analysis of electrical machines is desirable. Proficiency in MATLAB and simulink is necessary. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. The total duration is 3 months.

MERL is seeking an intern to work on coherent optical sensing. The ideal candidate would be an experienced PhD student or post-graduate researcher working in coherent sensing. The candidate should have a detailed knowledge of optical interferometry and imaging with a focus on either optical coherence tomography, optical coherence microscopy or FMCW LIDAR. Strong programming skills in Matlab are essential. Experience of working in an optical lab environment would be advantageous. Duration is 3 to 6 months.