Internship Openings

11 / 27 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.


  • CA1741: Learning for Connected Vehicles

    • MERL is seeking a highly motivated intern to collaborate with the Control for Autonomy team in the development of learning technologies for Connected Vehicles. The intern will conduct research in the development of methods for learning/optimization of Advanced Driver Assistance Systems (ADAS) using data-sharing between connected vehicles and/or infrastructure. The ideal candidate has knowledge of at least one of machine learning, estimation, connected vehicles, and vehicle control systems. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) is a plus. Good programming skills in Matlab are required and knowledge in Python or C/C++ is a merit. 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
  • CA1742: Mixed-Integer Programming for Motion Planning and Control

    • MERL is looking for a highly motivated individual to work on tailored computational algorithms and applications of mixed-integer programming for decision making, motion planning and control of hybrid systems. The research will involve the study and development of numerical optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving and robotics. The ideal candidate should have experience in either one or multiple of the following topics: branch-and-bound type methods, heuristics for mixed-integer programming (pre-solve, cutting planes, warm starting, integer-feasible solutions), modeling and formulation of MIPs for hybrid control systems, convex and non-convex optimization, machine learning and real-time optimization. PhD students in engineering or mathematics, especially with a focus on mixed-integer programming or numerical optimization, are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.

    • Research Areas: Control, Machine Learning, Optimization, Robotics
    • Host: Rien Quirynen
    • Apply Now
  • CV1738: Robot autonomous grasping using tactile sensing

    • The Computer Vision group is offering an internship opportunity in robot autonomous grasping using tactile sensing. The internship is open to highly skilled graduate students on a PhD track. Candidates should have a solid understanding of reinforcement learning, contact mechanics, simulating contacts, grasping, pose estimation and point cloud processing. The policies will be deployed on physical robots and the sensing is provided by various types of tactile sensing arrays. Strong programming skills are required, including MuJoCo, ROS, C++ and Python. Duration and start dates are flexible.

    • Research Areas: Computer Vision, Machine Learning, Robotics
    • Host: Radu Corcodel
    • Apply Now
  • ST1791: Single Pixel Imaging

    • The Computational Sensing team at MERL is seeking motivated and qualified individuals to design sensing mechanisms and develop algorithms that perform high quality image and video reconstruction from a single pixel detector. The project goal is to improve the performance and develop robust methods that can reduce the number of snapshots required for image formation. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: compressed sensing, imaging inverse problems, large-scale optimization, plug-and-play priors, learning-based modeling for imaging, learning theory for computational imaging. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

    • Research Areas: Computational Sensing, Machine Learning, Optimization, Signal Processing
    • Host: Hassan Mansour
    • Apply Now
  • ST1750: THz (Terahertz) Sensing

    • The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in THz (Terahertz) sensing. Expertise in statistical inference, unsupervised anomaly detection, and deep learning (spatial-temporal representation learning) is required. Previous hands-on experience in THz data analysis is a plus. Familiarity with python and deep learning libraries is a must. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with collaborators, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.

    • Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Optimization, Signal Processing
    • Host: Perry Wang
    • Apply Now
  • CI1468: Quantum Machine Learning

    • MERL is seeking an intern to work on research for quantum machine learning (QML). The ideal candidate is an experienced PhD student or post-graduate researcher having an excellent background in quantum computing, deep learning, and signal processing. Proficient programming skills with PyTorch, Qiskit, and PennyLane will be additional assets to this position.

    • Research Areas: Artificial Intelligence, Machine Learning, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • CI1752: Machine Learning for Electric Design Automation

    • MERL is seeking a highly motivated and qualified intern to join the Signal Processing group for an internship program. The ideal candidate will be expected to carry out research on machine learning for automated design synthesis to improve hardware efficiency of various digital signal processing algorithms. The candidate is expected to have solid knowledge of deep learning, reinforcement learning, symbolic learning, decision making, and graph neural networks. Hands-on experience of high-level synthesis, FPGA prototyping, verilog, and general digital signal processing is a plus.

    • Research Areas: Artificial Intelligence, Electric Systems, Machine Learning, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • 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
  • MD1757: ML based Digital Pre-distortion (DPD) for PA

    • MERL is looking for a talented intern to work on the next generation Digital-predistortion algorithms for power amplifier linearization such as 5G. The development of a DPD system involves aspects of signal processing and statistical algorithm design, RF components and instrumentation, digital hardware and software. It is therefore both a challenging and intellectually rewarding experience. This will involve MATLAB coding, interfacing to test equipment such as power sources, signal generators and analyzers and construction and calibration of RF component assemblies. The ideal candidate should have knowledge and experience in adaptive signal processing, machine learning, and radio communication. Good practical laboratory skills are needed. RF semiconductor devices and circuit knowledge is a plus. Duration is 3 to 6 months.

    • Research Areas: Electronic and Photonic Devices, Machine Learning, Signal Processing
    • Host: Rui Ma
    • Apply Now
  • MD1715: Electric Motor Fault Analysis

    • MERL is seeing a motivated and qualified individual to conduct research on electric machine fault analysis and detection. The ideal candidate should have solid background in electric machine theory, modeling, numerical analysis, operation, and fault detection techniques, including machine learning. Research experiences on modeling and analysis of electric machines and fault detection are required. Hands-on experience with permanent magnet motor design and analysis, and knowledge on machine learning are desirable. Senior Ph.D. students in related expertise are encouraged to apply. Start date for this internship is flexible.

    • Research Areas: Applied Physics, Machine Learning, Multi-Physical Modeling
    • Host: Bingnan Wang
    • Apply Now
  • MS1851: Dynamic Modeling and Control for Grid-Interactive Buildings

    • MERL is looking for a highly motivated and qualified candidate to work on modeling for smart sustainable buildings. The ideal candidate will have a strong understanding of modeling renewable energy sources, grid-interactive buildings, occupant behavior, and dynamical systems with expertise demonstrated via, e.g., peer-reviewed publications. Hands-on programming experience with Modelica is preferred. The minimum duration of the internship is 12 weeks; start time is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

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