Internship Openings

5 / 18 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.


  • 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

  • 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

  • CI0082: Internship - Quantum AI

    • MERL is excited to announce an internship opportunity in the field of Quantum Machine Learning (QML) and Quantum AI (QAI). We are seeking a highly motivated and talented individual to join our research team. This is an exciting opportunity to make a real impact in the field of quantum computing and AI, with the aim of publishing at leading research venues.

      Responsibilities:

      • Conduct cutting-edge research in quantum machine learning.
      • Collaborate with a team of experts in quantum computing, deep learning, and signal processing.
      • Develop and implement algorithms using PyTorch and PennyLane.
      • Publish research results at leading research venues.

      Qualifications:

      • Currently pursuing a PhD or a post-graduate researcher in a relevant field.
      • Strong background and solid publication records in quantum computing, deep learning, and signal processing.
      • Proficient programming skills in PyTorch and PennyLane are highly desirable.

      What We Offer:

      • An opportunity to work on groundbreaking research in a leading research lab.
      • Collaboration with a team of experienced researchers.
      • A stimulating and supportive work environment.

      If you are passionate about quantum machine learning and meet the above qualifications, we encourage you to apply. Please submit your resume and a brief cover letter detailing your research experience and interests. Join us at MERL and contribute to the future of quantum machine learning!

    • Research Areas: Artificial Intelligence, Machine Learning, Signal Processing, Applied Physics
    • Host: Toshi Koike-Akino
    • Apply Now

  • EA0069: Internship - PWM inverter switching loss reduction

    • MERL is looking for a self-motivated intern to work on PWM inverter simulation and design. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in power electronics, control, and optimization. Experience in switching loss reduction modulation is desired. The intern is expected to collaborate with MERL researchers to carry out simulations, optimize design, analyze results, and prepare manuscripts for scientific publications. The total duration is 3 months.

      Required Specific Experience

      • Experience with simulation tools for PWM inverter design.

    • Research Areas: Electric Systems, Signal Processing, Optimization
    • Host: Dehong Liu
    • Apply Now

  • EA0151: Internship - Physics-informed machine learning

    • MERL is looking for a self-motivated intern to work on physics-informed machine learning with application to electric machine condition monitoring and predictive maintenance. The ideal candidate would be a Ph.D. student in electrical engineering or computer science with solid research background in electric machines, signal processing, and machine learning. Proficiency in Python and Matlab is required. The intern is expected to collaborate with MERL researchers to build machine learning model for multi-modal data analysis, prepare technical reports, and draft manuscripts for scientific publications. The total duration is anticipated to be 3-6 months. The start date is flexible. This internship requires work that can only be done at MERL.

    • Research Areas: Electric Systems, Machine Learning, Multi-Physical Modeling, Signal Processing
    • Host: Dehong Liu
    • Apply Now