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.

Qualified applicants for MERL internships are individuals who have or can obtain full authorization to work in the U.S. and do not require export licenses to receive information about the projects they will be exposed to at MERL. The U.S. government prohibits the release of information without an export license to citizens of several countries, including, without limitation, Cuba, Iran, North Korea, Sudan and Syria (Country Groups E:1 and E:2 of Part 740, Supplement 1, of the U.S. Export Administration Regulations).


  • CV1375: Computer Vision for Robotic Manipulation

    • MERL is looking for a highly motivated intern to work on computer vision for robotic manipulation. There are several available topics to choose from including active perception, grasp detection, and intent recognition for human-robot interaction. The ideal candidate would be a Ph.D. student with a strong background in computer vision, deep learning, and/or robotics. Proficiency in Python programming is necessary and experience with ROS is a plus. Successful candidate will collaborate with MERL researchers to develop algorithms, conduct experiments, and prepare manuscripts for scientific publications. Start date is flexible and expected duration of the internship is at least 3 months. Interested candidates are encouraged to apply with their recent CV, list of related publications, and/or links to GitHub repositories (if any).

    • Research Areas: Computer Vision, Machine Learning, Robotics
    • Host: Siddarth Jain
    • Apply Now
  • DA1367: Safe reinforcement and deep learning algorithms

    • MERL is seeking a motivated and qualified individual to conduct research in safe reinforcement learning (RL) and deep learning algorithms for robotics applications. The ideal candidate should have solid background in RL. Knowledge of deep learning algorithms is a plus. Publication of the results produced during the internship is anticipated. Duration of the internship is expected to be 3 months. Start date is flexible.

    • Research Areas: Artificial Intelligence, Control, Robotics
    • Host: Mouhacine Benosman
    • Apply Now
  • CD1382: Motion Planning in Dynamic Environment

    • MERL is seeking a highly skilled and self-motivated intern to work on motion planning of nonholonomic system in dynamic environments. The ideal candidate should have solid backgrounds in task allocation, scheduling, and motion planning under dynamic and stochastic environment. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, computer science, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 3 months.

    • Research Areas: Control, Optimization, Robotics
    • Host: Yebin Wang
    • Apply Now
  • CD1383: Collaborative Estimation for Robotic Manipulators

    • MERL is seeking a highly skilled and self-motivated intern to conduct research on condition monitoring for robotic manipulators. The ideal candidate should have solid backgrounds in robotic manipulators, stochastic estimation methods for dynamical systems, and collaborative strategies over multi-agents. Experience of applying machine learning to dynamical systems is a strong plus. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, robotics, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 3 months.

    • Research Areas: Dynamical Systems, Machine Learning, Robotics
    • Host: Yebin Wang
    • Apply Now