Internship Openings

6 / 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.


  • CA0166: Internship - Spacecraft Guidance, Navigation, and Control

    • MERL is seeking a highly motivated graduate student for a research position in guidance, navigation, and control of spacecraft. The ideal candidate is a PhD student with strong experience in trajectory generation and sequential convex optimization, stochastic optimal control and state estimation, and astrodynamics and the three-body problem. Publication of results produced during the internship is expected. The expected duration of the internship is 3-6 months with a flexible start date.

      Required Specific Experience

      • Current enrollment in a PhD program in Aerospace, Mechanical, Electrical Engineering, or a related field
      • Familiarity with convex optimization solvers
      • Strong programming skills in Matlab, Python, and/or C/C++

    • Research Areas: Control, Dynamical Systems, Optimization
    • Host: Avishai Weiss
    • Apply Now

  • CA0170: Internship - Offroad Quadruped Robots

    • MERL is seeking a highly motivated intern to collaborate in the development of outdoor, offroad applications of quadruped robots, with wildlife monitoring and farming as examples. The overall project involves multiple developments including robust gait control, optimal gait generation in uncertain terrain conditions, planning and allocation of multiple robots. The work will be validated in simulation first, and experimental validation will be possible (if time permits) on robotic platforms on-site. The results of the internship are expected to be published in top-tier conferences and/or journals. The internship will take place during Fall/Winter 2025 (exact dates are flexible) with an expected duration of 3-6 months.

      Please use your cover letter to explain how you meet the following requirements, preferably with links to papers, code repositories, etc., indicating your proficiency.

      Required Experience

      • Current enrollment in a PhD program in Mechanical, Electrical, Aerospace Engineering, Computer Science or related programs, with a focus on Robotics and/or Control Systems
      • Experience in some/all of these topics:
        • Planning and control for legged robots
        • Modeling and control in offroad scenarios
        • ROS and simulation environment for robots control,
        • Strong programming skills in Python and/or C/C++

      Additional Useful Experience

      • Modeling of terrain uncertaint
      • Robust control and planning under uncertainty
      • Coverage control in uncertain scenarios
      • Experience with computer vision

    • Research Areas: Control, Robotics, Dynamical Systems, Optimization
    • Host: Stefano Di Cairano
    • Apply Now

  • CA0153: Internship - High-Fidelity Visualization and Simulation for Space Applications

    • MERL is seeking a highly motivated graduate student to develop high-fidelity full-stack GNC simulators for space applications. The ideal candidate has strong experience with rendering engines, synthetic image generation, and computer vision, as well as familiarity with spacecraft dynamics, motion planning, and state estimation. The developed software should allow for closed-loop execution with the synthetic imagery, and ideally allow for real-time visualization. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.

      Required Specific Experience

      • Current enrollment in a graduate program in Aerospace, Computer Science, Robotics, Mechanical, Electrical Engineering, or a related field

      • Experience with one or more of Blender, Unreal, Unity, along with their APIs

      • Strong programming skills in one or more of Matlab, Python, and/or C/C++

    • Research Areas: Computer Vision, Control, Dynamical Systems, Optimization
    • Host: Avishai Weiss
    • Apply Now

  • CA0157: Internship - Spatio-temporal monitoring using mobile robots

    • MERL is seeking a highly motivated intern to collaborate and develop a framework for spatio-temporal monitoring using heterogeneous mobile robots. The work will involve multi-domain research, including multi-agent planning and control, optimization, adaptive and learning-based control, and computer vision. The methods will be implemented and evaluated using physical experiments on robotic platforms (e.g., Crazyflies,Turtlebots). The results of the internship are expected to be published in top-tier conferences and/or journals. The internship will take place during Fall/Winter 2025 (exact dates are flexible) with an expected duration of 4-6 months.

      Please use your cover letter to explain how you meet the following requirements, preferably with links to papers, code repositories, etc., indicating your proficiency.

      Required Specific Experience

      • Current enrollment in a PhD program in Mechanical, Electrical Engineering, Computer Science, or related programs, with a focus on Robotics and/or Control Systems
      • Experience in some/all of these topics: multi-agent planning and control, optimization, adaptive and learning-based control, and computer vision
      • Experience with ROS2 and validation of algorithms on robotic platforms
      • Strong programming skills in Python and/or C/C++

      Desired Specific Experience

      • Experience with Crazyflie quadrotors and the Crazyswarm2 library
      • Experience with cvxpy and/or gurobipy
      • Experience in convex optimization and model predictive control
      • Experience with computer vision

    • Research Areas: Control, Dynamical Systems, Robotics, Optimization, Artificial Intelligence
    • Host: Abraham Vinod
    • Apply Now

  • CA0165: Internship - Optimization of Aerial Robot Coordination

    • MERL is seeking a self-motivated and qualified individual to work on developing an integer/mixed-integer programming solver customarily designed for coordination planning of aerial drones. The ideal candidate will be a PhD student in computer science, mathematics, industrial engineering, or a related discipline, with a solid background in integer optimization. Preferred skills include knowledge of branch-price-and-cut algorithm or column generation, and hands-on experience with callbacks of the Gurobi Optimizer; strong programming skills and experience with at least one of Python, Julia, C/C++, Matlab are also expected. Publication of results produced during the internship is desired. The expected start date is in Fall 2025 or Spring 2026, for a duration of 3- months.

      Required Specific Experience

      • Significant hands-on experience with integer optimization.
        • Experience with trajectory optimization is a plus.
      • Fluency in at least one of: Python, Julia, C/C++, Matlab
      • Completed their MS, or >30% of their PhD program

    • Research Areas: Artificial Intelligence, Control, Optimization, Robotics, Dynamical Systems
    • Host: Kento Tomita
    • Apply Now

  • 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