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.


  • 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

  • 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

  • ST0141: Internship - Uncertainty Quantification in Computational Physics

    • The Computational Sensing team at MERL is seeking a highly motivated PhD student for an internship focused on uncertainty quantification (UQ) in computational modeling of physical systems. The goal of this project is to advance the methodology and practice of UQ, with a focus on reduced-order stochastic modeling and optimal sensor placement for Bayesian inverse problems. The research will draw upon foundational ideas and techniques in applied mathematics and statistics for applications in wave propagation, fluid dynamics, and more generally high-dimensional systems. The ideal candidate will be a PhD student in engineering, applied mathematics, computer science, or related fields with a solid background and publication record in any of the following areas: stochastic modeling, dimensionality reduction, Bayesian inference, optimal experimental design, and tensor methods. Programming skills in Python or MATLAB are required. Publication of the results obtained during the internship is expected. The duration is anticipated to be at least 3 months with a flexible start date.

    • Research Areas: Computational Sensing, Dynamical Systems, Applied Physics, Machine Learning, Optimization
    • Host: Wael Ali
    • Apply Now