Internship Openings

17 Intern positions are currently open.

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).


  • DA1344: Learning from Demonstration (LfD) for Robotics

    • MERL is looking for a highly motivated intern to work on developing algorithms for robot learning using learning from demonstration, imitation learning and/or deep reinforcement learning. Successful candidate will collaborate with MERL researchers to design, analyze, and implement new algorithms, conduct experiments, and prepare results for publication. The candidate should have a strong background in (deep) reinforcement learning, Imitation Learning (or Learning from Demonstrations, LfD), machine learning and robotics. Prior experience of working with robotic systems is required. The candidate should be comfortable implementing the developed algorithms in Python and should have prior experience working with ROS. Prior exposure to deep learning and hands-on experience with packages such as Pytorch and/or Tensorflow is expected. The candidate is expected to be a PhD student in Computer Science, Electrical Engineering, Operations Research, Statistics, Applied Mathematics, or a related field, with relevant publication record. Expected duration of the internship is at least 3 months. The position is expected to be available starting late August or early September. Interested candidates are encouraged to apply with their recent CV with list of related publications and links to GitHub repositories (if any).

    • Research Areas: Artificial Intelligence, Machine Learning, Robotics
    • Host: Devesh Jha
    • Apply Now
  • SP1305: Intelligent error correction coding

    • The Signal Processing group at MERL is seeking a highly motivated, qualified individual to join our 3-month internship program of research on error correction coding for digital communications. The ideal candidate is expected to possess an excellent background in channel coding theory, source coding, information theory, coded modulation design, signal processing, and machine learning. Strong C/C++ skill and GPU/FPGA acceleration are plus. Candidates who hold a PhD or in their senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications
    • Host: Toshi Koike-Akino
    • Apply Now
  • SP1155: Coexistence of the Heterogeneous Wireless Technologies

    • MERL is seeking a highly motivated, qualified intern to join the Electronics and Communications group for a three month internship program. The ideal candidate will be expected to carry out research on coexistence of the heterogeneous wireless technologies in the Sub-1 GHz (S1G) band. The candidate is expected to develop innovative coexistence technology for IEEE 802.15.4g to mitigate interference caused by other S1G technologies such as IEEE 802.11ah, LoRa and SigFox. The candidates should have knowledge of 802.15.4g and 802.11ah protocols. Additionally, the candidate should also be familiar with NS3 simulators. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications, Signal Processing
    • Host: Jianlin Guo
    • Apply Now
  • SP1307: Vehicular traffic environment sensing

    • MERL is seeking a highly motivated, qualified intern to join a three month internship program. The ideal candidate will be expected to carry out research on environmental sensing in high frequency bands. The candidate is expected to develop innovative sensing technologies. Candidates should have strong knowledge about neural network and learning techniques, such as machine learning, deep learning, shallow learning, and distributed learning. In addition, understanding of spectrum sensing and wireless communications technologies is necessary. Proficient programming skills with Python, 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: Signal Processing
    • Host: K.J. Kim
    • Apply Now
  • SA1031: Distributed auctions for network welfare maximization

    • We are looking for a talented individual to collaborate and facilitate research on new algorithms in mechanism design and distributed auctions. Responsibilities will include mathematical modeling, algorithm design, software prototyping, and running Monte Carlo simulations in a network traffic domain. Candidates should be strong scientific programmers and have some background in numerical optimization, simulation design, and auction theory.

    • Research Areas: Optimization
    • Host: Matt Brand
    • Apply Now
  • SA1320: Deep Network Information Security

    • We are seeking graduate students interested in helping advance the field of network and information security using the latest developments in deep learning. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. The ideal candidate would be a senior Ph.D. student with experience in deep networks, natural language processing, and network security. The duration of the internship is expected to be 3-6 months. The internship can be scheduled either during the summer or at another time of year.

    • Research Areas: Machine Learning, Speech & Audio
    • Host: Bret Harsham
    • Apply Now
  • CV1283: Understanding of Deep Learning

    • MERL is looking for a self-motivated intern to work on understanding of deep learning. The ideal candidate would be a Ph.D. student with a strong background in machine learning, optimization and computer vision. Proficiency in deep learning toolbox such as PyTorch and Tensorflow is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.

    • Research Areas: Computer Vision, Machine Learning, Optimization
    • Host: Ziming Zhang
    • Apply Now
  • CV1303: Generative Adversarial Networks (GANs)

    • Generative adversarial networks (GANs) and related methods have generated much excitement for their ability to synthesize images and data that appear remarkably realistic. MERL is seeking a highly motivated intern to conduct original research in the area of generative adversarial networks. The successful candidate will collaborate with MERL researchers to design and implement new models, conduct experiments, and prepare results for publication. The ideal candidate would be a senior PhD student in computer vision with experience in GANs and related deep learning methods, as well as strong general knowledge in machine learning. Strong programming skills in Python, flexibility working across various deep learning platforms (e.g., TensorFlow and PyTorch), and previous experience coding GANs are expected.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Tim Marks
    • Apply Now
  • CV1269: Machine Learning for Computer Vision

    • MERL is looking for a self-motivated intern to work on machine learning for computer vision. There are several available topics to choose from. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision. Proficiency in Python programming is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Jeroen van Baar
    • Apply Now
  • CD1260: Model Predictive Control of Hybrid Systems

    • The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to work on hybrid model predictive control. The scope of work includes the development of model predictive control algorithms for hybrid dynamical systems, switched systems, and quantized systems, analysis and property proving, and applications in automotive, space systems, and energy systems. PhD students with expertise in some among control, optimization, model predictive control and hybrid systems, and with working knowledge of Matlab implementation are welcome to apply. The expected duration of the internship is 3-6 months with flexible start date.

    • Research Areas: Control, Dynamical Systems, Optimization
    • Host: Stefano Di Cairano
    • Apply Now
  • CD1274: Motion Planning and Experiment

    • MERL is seeking a highly skilled and motivated intern to work on motion planning and tracking control of nonholonomic systems in dynamic environments. The ideal candidate should have solid backgrounds in motion planning algorithms, their implementation, and experiment validation. Demonstrated capability in strong coding and publishing results in leading conference and journals is a necessity. 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, Robotics
    • Host: Yebin Wang
    • Apply Now
  • CD1140: MPC-based modular control architectures

    • MERL's Mechatronics group is seeking a highly motivated intern for performing research in Optimization based modular control architectures. The ideal candidate is working towards a Ph.D. in electrical, mechanical, or aerospace engineering, or in computer science, and has background in model predictive control, optimization, set-based methods, and modular control architectures. The candidate is expected to possess strong abilities in theorem proving, and algorithm development, analysis, and Matlab implementation. The internship start date is flexible and the duration is approximately 3 months, with possible extension. Publication of the results produced during the internship is expected.

    • Research Areas: Control
    • Host: Stefano Di Cairano
    • Apply Now
  • CD1300: Compiler Optimizations for Linear Algebra Kernels

    • MERL is looking for a highly motivated individual to work on automatic, compiler based techniques for optimizing linear algebra kernels. The ideal candidate is a Ph.D. student in computer science with extensive experience in compiler design and source code optimization techniques. In particular, the successful candidate will have a strong working knowledge of polyhedral optimization techniques, the LLVM compiler, and Polly. Strong C/C++ skills and knowledge of LLVM at the source level are required. Publication of results in conference proceedings and journals is expected. The expected duration of the internship is 3 months and the start date is flexible.

    • Research Areas: Control, Machine Learning, Optimization
    • Host: Bram Goldsmith
    • Apply Now
  • CD1257: Autonomous vehicles Planning and Control

    • The Control and Dynamical Systems (CD) group at MERL is seeking highly motivated interns at varying expertise levels to conduct 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. PhD students will be considered for algorithm development and analysis, and property proving. Master students will be considered for development and implementation in a scaled robotic test bench for autonomous vehicles. For algorithm development and analysis it is highly desirable to have deep background in one or more among: sampling-based planning methods, particle filtering, model predictive control, reachability methods, formal methods and abstractions of dynamical systems, and experience with their implementation in Matlab/Python/C++. For algorithm implementation, it is required to have working knowledge of Matlab, C++, and ROS, and it is a plus to have background in some of the above mentioned methods. The expected duration of the internship is 3-6 months with flexible start date.

    • Research Areas: Artificial Intelligence, Control, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CD1255: Speed-sensorless motor control

    • MERL is seeking a motivated and qualified individual to conduct research in control of electromechanical systems. The ideal candidate should have solid backgrounds in control and estimation for electrical machines, demonstrated capability to publish results in leading conferences/journals, and experience with real-time control experiments involving high power devices. Senior Ph.D. students are encouraged to apply. Start date for this internship is around May 2019 and the duration is 3 months.

    • Research Areas: Control, Dynamical Systems, Electric Systems
    • Host: Yebin Wang
    • Apply Now
  • MP1263: Fault analysis for electric motors

    • MERL is seeking a highly motivated intern to conduct research in electric machine fault analysis. The ideal candidate should be a senior Ph. D student in Electrical Engineering or related discipline with a solid background in the physics and engineering of electric motors, and early fault detection. Knowledge and experience in electric motor modeling and machine learning are desired. The candidate is expected to collaborate with MERL researchers to conduct theoretical analysis, numerical simulations, develop algorithms and prepare manuscripts for scientific publications. The duration of internship is expected to be 3 months and start date is flexible.

    • Research Areas: Applied Physics, Electric Systems, Multi-Physical Modeling
    • Host: Bingnan Wang
    • Apply Now
  • MP1262: Thermal modeling for electric motors

    • MERL is looking for a qualified intern to conduct research on thermal modeling and temperature estimation for electric motors. The ideal candidate should have solid background in the physics and engineering of electric machines, in particular the magnetic field calculations, and loss modeling. Related experience on control and estimation theory is a plus. The candidate is expected to collaborate with MERL researchers to conduct theoretical analysis, numerical simulations, develop algorithms and prepare manuscripts for scientific publications. Senior PhD students in electrical engineering, mechanical engineering, and other related areas are encouraged to apply. The duration of the internship is about 3 months.

    • Research Areas: Applied Physics, Dynamical Systems, Multi-Physical Modeling
    • Host: Bingnan Wang
    • Apply Now