Internship Openings

6 / 23 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.

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 and Syria (Country Groups E:1 and E:2 of Part 740, Supplement 1, of the U.S. Export Administration Regulations).

Rising to the challenges of COVID-19

MERL believes that having an internship be located in MERL's office allows for particularly good interaction between you and those that you will be working with at MERL. In addition, some intern projects, e.g., ones that require specialized laboratory equipment, can only be pursued in our office. Going forward, we expect that all internships will be in-person at MERL. If health and safety concerns do not permit this, we will reevaluate our plans and some internships might have to become remote.

It is a requirement at MERL that everyone working in MERL's space must be fully vaccinated. In order for you to have your internship at MERL, you will have to prove that you are fully vaccinated when you arrive at MERL, i.e., by showing your vaccination card.


  • CI1733: ML for GNSS-based Applications

    • MERL is seeking a highly motivated, qualified intern to work on machine learning for Global Navigation Satellite System (GNSS) applications. The ideal candidate is working towards a PhD and is expected to develop innovative machine learning technologies to increase accuracy and integrity of GNSS-based positioning systems. Candidates should have strong knowledge about as many as possible of GNSS signal processing for multipath mitigation, handling RINEX data, neural network and learning techniques, such as feature extraction, deep machine learning, reinforcement learning, domain adaptation, and distributed learning. Proficient programming skills with PyTorch, Matlab, and C++, and strong mathematical analysis will be additional assets to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications, Dynamical Systems, Machine Learning, Signal Processing
    • Host: K.J. Kim
    • Apply Now
  • CA1869: Learning for Connected Vehicles and Smart Cities

    • MERL is seeking a research intern to collaborate with the Control for Autonomy team in the development of learning for connected vehicles and/or smart cities. The intern will develop technologies for optimizing Advanced Driver Assistance Systems (ADAS) and/or for learning road conditions and to leverage such information using data-sharing between vehicles. The ideal candidate has knowledge of at least one of machine learning, statistical estimation, connected vehicles, and vehicle control systems. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, etc.) is a plus. Good programming skills in Matlab or Python are required. PhD students in engineering, mathematics, or similar are encouraged to apply. The expected duration of the internship is 3-6 months. The start date is flexible.

    • Research Areas: Control, Dynamical Systems, Machine Learning
    • Host: Marcel Menner
    • Apply Now
  • CA1707: Autonomous vehicles guidance and control

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles. The research domain includes algorithms for path planning, vehicle control, high level decision making, sensor-based navigation, driver-vehicle interaction. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, predictive control algorithms linear and nonlinear systems, motion planning, convex, non-convex, and mixed -integer optimization, statistical estimation, cooperative control. Good programming skills in MATLAB, Python or C/C++ are required, knowledge of rapid prototyping systems, automatic code generation or ROS is a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.

    • Research Areas: Control, Dynamical Systems, Optimization
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1728: Safe data-driven control of dynamical systems under uncertainty

    • MERL is looking for a highly motivated individual to work on safe control of data-driven, uncertain, dynamical systems. The research will develop novel optimization and learning-based control algorithms to guarantee safety and performance in various industrial applications, including autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: optimal control under uncertainty, (robust and stochastic) model predictive control, (convex and non-convex) optimization, and (reinforcement and statistical) learning. Ph.D. students in engineering or mathematics with a focus on control, optimization, and learning are encouraged to apply. A successful internship will result in submission of relevant results to peer-reviewed conference proceedings and journals, and development of well-documented (Python/MATLAB) code for MERL. The expected duration of the internship is 3-6 months, and the start date is Summer 2022.

    • Research Areas: Artificial Intelligence, Control, Dynamical Systems, Optimization, Robotics
    • Host: Abraham Vinod
    • Apply Now
  • CA1864: Motion planning and control: Design and experimental validation

    • MERL is seeking a highly motivated intern to collaborate in the development and experimental validation of control and motion planning methods in various robotic testbeds (quadrotors and mini-cars) at MERL. The ideal candidate is enrolled in a Masters/PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in motion planning, control, optimization and their application in mobile robots, including experimental validation. The successful candidate is proficient in ROS, C/C++ and Python, and at least familiar with MATLAB. The expected duration of the internship is at least 3 months with possible extensions and with a flexible start date in the Fall 2022.

    • Research Areas: Control, Dynamical Systems, Robotics
    • Host: Abraham Vinod
    • Apply Now
  • MS1866: Deep Unsupervised/Semi-Supervised Learning for Smart Buildings

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Multiphysical Systems (MS) team in research on unsupervised/semi-supervised learning using data from real building energy systems. The ideal candidate is expected to be working towards a Ph.D. in deep learning for time-series, with special interest in learning representations for deep clustering. Fluency in Python and either PyTorch/Tensorflow is required. Previous peer-reviewed publications in related research topics and/or experience with mining from real-world data is preferred. The minimum duration of the internship is 12 weeks; start time is flexible.

    • Research Areas: Artificial Intelligence, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Signal Processing
    • Host: Ankush Chakrabarty
    • Apply Now