Internship Openings

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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.


  • ST0105: Internship - Surrogate Modeling for Sound Propagation

    • MERL is seeking a motivated and qualified individual to work on fast surrogate models for sound emission and propagation from complex vibrating structures, with applications in HVAC noise reduction. The ideal candidate will be a PhD student in engineering or related fields with a solid background in frequency-domain acoustic modeling and numerical techniques for partial differential equations (PDEs). Preferred skills include knowledge of the boundary element method (BEM), data-driven modeling, and physics-informed machine learning. Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.

    • Research Areas: Artificial Intelligence, Dynamical Systems, Machine Learning, Multi-Physical Modeling
    • Host: Saviz Mowlavi
    • 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

  • MS0102: Internship - Estimation and Calibration of Multi-physical Systems Using Experiments

    • MERL is looking for a highly motivated and qualified candidate to work on estimation and calibration of muti-physical systems governed by differential algebraic equations (DAEs). The research will involve study, development and efficient implementation of estimation/calibration approaches for large-scale nonlinear systems, e.g., vapor compression cycles, with limited experimental data. The ideal candidate will have a strong background in one or multiple of the following topics: nonlinear control and estimation, optimization, and model calibration; with expertise demonstrated via, e.g., peer-reviewed publications. Prior experience in working with experimental data, and programming in Julia/Modelica is a plus. Senior PhD students in mechanical, electrical, chemical engineering or related fields are encouraged to apply. The typical duration of internship is 3 months, and the start date is flexible.

      Required Specific Experience

      • Graduate student with 2+ years of relevant research experience

      Additional Desired Experience

      • Strong programming skills in Julia or Modelica
      • Prior experience in working with thermofluid systems
      • Prior experience in estimation/calibration of complex nonlinear systems using experimental data

    • Research Areas: Multi-Physical Modeling, Optimization, Control, Dynamical Systems, Applied Physics
    • Host: Vedang Deshpande
    • Apply Now

  • MS0098: Internship - Control and Estimation for Large-Scale Thermofluid Systems

    • MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems for process applications. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in control and estimation, numerical methods, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 3 months.

    • Research Areas: Optimization, Machine Learning, Control, Multi-Physical Modeling
    • Host: Chris Laughman
    • Apply Now