Internship Openings

3 / 22 Intern positions were found.

Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

MERL expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of MERL's employees to perform their job duties may result in discipline up to and including discharge.

Working at MERL requires full authorization to work in the U.S and access to technology, software and other information that is subject to governmental access control restrictions, due to export controls. Employment is conditioned on continued full authorization to work in the U.S and the availability of government authorization for the release of these items, which might include without limitation, obtaining an export license or other documentation. MERL may delay commencement of employment, rescind an offer of employment, terminate employment, and/or modify job responsibilities, compensation, benefits, and/or access to MERL facilities and information systems, as MERL deems appropriate, to ensure practical compliance with applicable employment law and government access control restrictions.


  • ST0096: Internship - Multimodal Tracking and Imaging

    • MERL is seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus.

      Required Specific Experience

      • Experience with Python and Python Deep Learning Frameworks.
      • Experience with FMCW radar and/or Depth Sensors.

    • Research Areas: Computer Vision, Machine Learning, Signal Processing, Computational Sensing
    • Host: Petros Boufounos
    • Apply Now

  • ST0116: Internship - Deep Learning for Radar Perception

    • The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in radar perception. Expertise in deep learning-based object detection, pose estimation, segmentation, multiple object tracking (MOT), and representation learning on radar data is required. Previous hands-on experience with open indoor and outdoor radar datasets is a plus. Familiarity with basic radar concepts and MERL's recent work in radar perception is an asset. The intern will work closely with MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, and prepare results for patents and publication. The internship is expected to last 3 months with a preferred start date after June 2025.

      Required Specific Experience

      • Solid understanding of state-of-the-art perception frameworks including transformer-based (e.g., DETR) and diffusion-based (e.g., DiffusionDet) methods.
      • Hands-on experience with open large-scale radar datasets such as MMVR, HIBER, RADIATE, and K-Radar.
      • Proficiency in Python and experience with job scheduling on GPU clusters using tools like Slurm.
      • Proven publication records in top-tier venues such as CVPR, ICCV, ECCV, NeurIPS.
      • Knowledge of basic radar concepts such as FMCW, MIMO, (micro-) Doppler signature, radar point clouds, heatmaps, and raw ADC waveforms.
      • Familiarity with MERL's recent radar perception research such as TempoRadar, SIRA, MMVR, and RETR.

    • Research Areas: Computational Sensing, Signal Processing
    • Host: Perry Wang
    • Apply Now

  • ST0081: Internship - Optical Sensing for Airflow Reconstruction

    • The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that can perform background oriented schlieren (BOS) tomography. The project goal is to utilize both analytical and learning-based architectures to enable the reconstruction of 3D air flows in an indoor setting from BOS measurements coupled with physics informed machine learning. 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, differentiable scene rendering, learning-based modeling for imaging, and physics informed neural networks. Preferred skills include experience with schlieren tomography, inverse rendering, neural scene representation, and computational imaging hardware. 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.

      Required Specific Experience

      • Experience with differentiable/physics-based rendering.

    • Research Areas: Computational Sensing, Machine Learning, Signal Processing
    • Host: Hassan Mansour
    • Apply Now