Internship Openings

4 / 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.


  • EA0149: Internship - Electric Motor Design Optimization

    • MERL is seeking a motivated and qualified individual to conduct research on physics informed neural network-based modeling for electric motor design optimization. Ideal candidates should be Ph.D. students with solid background and proven publication record in one or more of the following research areas: 2D/3D electromagnetic modeling and simulation, analytical modeling methods for electromagnetics and iron losses (e.g. magnetic equivalent circuit), and machine learning-based surrogate modeling. Strong coding skill with ANSYS or open-source FEM software and Python-based learning library is a must and prior experience with running jobs over cluster is a plus. Start date for this internship is flexible and the duration is 3-6 months.

      Required Specific Experience

      • Experience with modeling and simulations for motor design

    • Research Areas: Electric Systems, Multi-Physical Modeling, Optimization
    • Host: Bingnan Wang
    • Apply Now

  • EA0073: Internship - Fault Detection for Electric Machines

    • MERL is seeking a motivated and qualified individual to conduct research on electric machine fault analysis and detection methods. Ideal candidates should be Ph.D. students with a solid background and publication record in one more research area on electric machines: electric and magnetic modeling, machine design and prototyping, harmonic analysis, fault detection, and predictive maintenance. Knowledge on data analysis and machine learning algorithms, and strong programming skills using Python/PyTorch are expected. Research experience on modeling and analysis of electric machines and fault diagnosis is desired. Senior Ph.D. students in related expertise, such as electrical engineering, mechanical engineering, and applied physics are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.

    • Research Areas: Electric Systems, Machine Learning, Multi-Physical Modeling
    • Host: Bingnan Wang
    • 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

  • 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