Internship Openings

22 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.

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.


  • CV2119: Conditional Video Generation

    • We seek a highly motivated intern to conduct original research in generative models for conditional video generation. We are interested in applications to various tasks such as video generation from text, images, and diagrams. The successful candidate will collaborate with MERL researchers to design and implement new models, conduct experiments, and prepare results for publication. The candidate should be a PhD student (or postdoc) in computer vision and machine learning with a strong publication record including at least one paper in a top-tier computer vision or machine learning venue such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, AAAI, or TPAMI. Strong programming skills, experience developing and implementing new models in deep learning platforms such as PyTorch, and broad knowledge of machine learning and deep learning methods are expected, including experience in the latest advances in conditional video generation. Start date is flexible; duration should be at least 3 months.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Tim Marks
    • Apply Now
  • CV2089: Visual Localization and Mapping

    • MERL is looking for a highly motivated intern to work on an original research project on visual localization and mapping. A strong background in 3D computer vision is required. Experience in robot vision and/or deep learning will be valued. The successful candidate is expected to have published at least one paper in a top-tier computer vision or robotics venues, such as CVPR, ECCV, ICCV, ICRA, IROS, or RSS, along with solid programming skills in Python and/or C/C++. The position is available for graduate students on a Ph.D. track. Duration and start dates are flexible.

    • Research Area: Computer Vision
    • Host: Pedro Miraldo
    • Apply Now
  • ST1763: Technologies for Multimodal Tracking and Imaging

    • MERL is seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus.

    • Research Areas: Computational Sensing, Signal Processing
    • Host: Petros Boufounos
    • Apply Now
  • ST1762: Computational Sensing Technologies

    • The Computational Sensing team at MERL is seeking motivated and qualified individuals to assist in the development of computational methods for a variety of sensing applications. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, deep learning for inverse problems, large-scale optimization, blind inverse scattering, radar/lidar/THz imaging, joint communications and sensing, multimodal sensor fusion, object or human tracking, sensing of dynamical systems, or wave-based inversion. Experience with experimentally measured data is desirable. 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, Signal Processing
    • Host: Petros Boufounos
    • Apply Now
  • ST2090: Radiation Source Localization

    • The Computational Sensing Team at MERL is seeking an intern to work on estimation algorithms for radioactive source localization. The candidate should have experience with statistical modeling and estimation theory. A detailed knowledge of interactions of particles with matter, imaging inverse problems, and/or computed tomography is preferred. Hands-on experience with high-energy physics simulators (e.g., Geant4) is beneficial but not required. Strong programming skills in Python are essential. Publication of the results produced during our internships is expected. The duration is anticipated to be 3-6 months.

    • Research Areas: Applied Physics, Computational Sensing, Electronic and Photonic Devices, Signal Processing
    • Host: Joshua Rapp
    • Apply Now
  • ST2083: Deep Learning for Radar Perception

    • The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in radar perception. Expertise in deep learning-based object detection, multiple object tracking, data association, and representation learning (detection points, heatmaps, and raw radar waveforms) is required. Previous hands-on experience on open indoor/outdoor radar datasets is a plus. Familiarity with the concept of FMCW, MIMO, and range-Doppler-angle spectrum is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, 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, Computer Vision, Dynamical Systems, Machine Learning, Optimization, Signal Processing
    • Host: Perry Wang
    • Apply Now
  • CI2049: Efficient/Green AI

    • MERL is seeking highly motivated and qualified interns to work on efficient machine learning techniques. The ideal candidates would have significant research experience in federated learning, generative large language models, and efficient/green AI. A mature understanding of modern machine learning methods, proficiency with Python, and familiarity with deep learning frameworks are expected. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. The expected duration is 3 months long with flexible start dates.

    • Research Area: Artificial Intelligence
    • Host: Toshi Koike-Akino
    • Apply Now
  • CI2075: Human-Machine Interface with Biosignal Processing

    • MERL is seeking an intern to work on research for human-machine interface with multi-modal bio-sensors. The ideal candidate is an experienced PhD student or post-graduate researcher having an excellent background in brain-machine interface (BMI), deep learning, mixed reality (XR), remote robot manipulation, bionics, and bio sensing. The expected duration of the internship is 3-6 months, with a flexible start date.

    • Research Areas: Artificial Intelligence, Machine Learning, Robotics
    • Host: Toshi Koike-Akino
    • Apply Now
  • CI2091: Robust AI for Operational Technology Security

    • MERL is seeking a highly motivated and qualified intern to work on operational technology security. The ideal candidate would have significant research experience in cybersecurity for operational technology, anomaly detection, robust machine learning, and defenses against adversarial examples. A mature understanding of modern machine learning methods, proficiency with Python, and familiarity with deep learning frameworks are expected. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. The expected duration is 3 months with flexible start dates.

    • Research Areas: Artificial Intelligence, Machine Learning
    • Host: Ye Wang
    • Apply Now
  • CA2182: Motion Planning and Control for Articulated Vehicles

    • MERL is seeking a highly skilled and self-motivated intern to work on motion planning of articulated vehicles. The ideal candidate should have solid backgrounds in established path/motion planning algorithms (A*, D*, graph-search) and optimization-based control for ground and articulated vehicles. Excellent coding skills in MATLAB/Simulink and publication records are necessary. Experience with CasADi and dSPACE is a plus. Ph.D. students in robotics, computer science, control, electrical engineering, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 4-6 months.

    • Research Areas: Control, Dynamical Systems, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1940: Autonomous vehicle planning and contro in uncertain environments

    • 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 in uncertain surrounding environments. The research domain includes algorithms for path planning and control in environments that are uncertain and perceived by sensing and predicted according to models and data. 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, sensor uncertainty modeling, data-driven prediction, predictive control for uncertain systems, motion planning. Good programming skills in MATLAB, Python are required, knowledge of C/C++, rapid prototyping systems, automatic code generation, vehicle simulation packages (CarSim, CarMaker) or ROS are 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, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CA2132: Optimization Algorithms for Motion Planning and Predictive Control

    • MERL is looking for a highly motivated and qualified individual to work on tailored computational algorithms for optimization-based motion planning and predictive control applications in autonomous systems (vehicles, mobile robots). The ideal candidate should have experience in either one or multiple of the following topics: convex and non-convex optimization, stochastic predictive control (e.g., scenario trees), interaction-aware motion planning, machine learning, learning-based model predictive control, mathematical programs with complementarity constraints (MPCCs), optimal control, and real-time optimization. PhD students in engineering or mathematics, especially with a focus on research related to any of the above topics are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3 months, and the start date is flexible.

    • Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CA2131: Collaborative Legged Robots

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on control and planning algorithms for legged robots for support activities of and collaboration with humans. The ideal candidate is expected to be working towards a PhD with strong emphasis in robotics control and planning and to have interest and background in as many as possible of: motion planning algorithms, control for legged robot locomotions, legged robots, perception and sensing with multiple sensors, SLAM, vision-based control. Good programming skills in Python or C/C++ are required. The expected start of of the internship is flexible, with duration of 3--6 months.

    • Research Areas: Control, Dynamical Systems, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • EA2120: AI-assisted Design of Semiconductor Devices

    • We are seeking a graduate student interested in the research of AI-assisted design of semiconductor devices in general and GaN, SiC and Si IGBT in particular. The interns will collaborate with researchers at MERL and those in Japan to explore and develop new AI input models and methodology, and optimization methods, using both simulated and experimental data for the AI-assisted design of semiconductor devices. The ideal candidates are senior Ph.D. students with experience in semiconductor device physics, device modeling, deep learning, and other machine learning techniques, and the use of TCAD as a simulation tool. Those with deep knowledge of GaN, Si, and SiC devices and applications in RF and power electronics will be great assets. This internship's Start date is flexible and lasts 3-6 months.

    • Research Areas: Electronic and Photonic Devices, Machine Learning
    • Host: Koon Hoo Teo
    • Apply Now
  • EA2168: Blockchain Solutions for Factory Automation 2

    • MERL is seeking an intern to work on blockchain based solutions for factory automation. The ideal candidate will have experience implementing a blockchain network with Hyperledger Fabric, have experience working with smart contracts, and be fluent languages common in the blockchain space such as js, Go, etc. Experience deploying cloud based applications and docker is highly desirable. The start date is flexible and the duration is 3-4 months.

    • Research Area: Electric Systems
    • Host: Bram Goldsmith
    • Apply Now
  • EA2050: Electric Motor Design and Electromagnetic Analysis

    • MERL is seeing a motivated and qualified individual to conduct research on electric motor design and modeling, with a strong focus on electromagnetic analysis. Ideal candidates should be Ph.D. students with solid background and publication record in one more research area on electric machines: electric and magnetic modeling, new machine design and prototyping, harmonic analysis, fault detection, and predictive maintenance. Research experiences on modeling and analysis of electric machines and fault diagnosis are required. Hands-on experience with new motor design and data analysis techniques are highly desirable. Start date for this internship is flexible and the duration is 3-6 months.

    • Research Areas: Applied Physics, Multi-Physical Modeling
    • Host: Bingnan Wang
    • Apply Now
  • SA2181: Autonomous mobile robot data collection

    • MERL is seeking a highly motivated intern to collaborate in the collection of data for sensing, planning, and control methods in a robotic test-bed using Turtlebots 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, computer vision, and their application in mobile robots, including experimental validation. The candidate should be proficient in ROS, C/C++, and Python. The expected duration of the internship is 1-2 months, with a flexible start date in early summer to fall.

    • Research Area: Robotics
    • Host: Chiori Hori
    • Apply Now
  • SA2073: Multimodal scene-understanding

    • We are looking for a graduate student interested in helping advance the field of multimodal scene understanding, with a focus on scene understanding using natural language for robot dialog and/or indoor monitoring using a large language model. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern''s doctoral work. The ideal candidates are senior Ph.D. students with experience in deep learning for audio-visual, signal, and natural language processing. Good programming skills in Python and knowledge of deep learning frameworks such as PyTorch are essential. Multiple positions are available with flexible start date (not just Spring/Summer but throughout 2024) and duration (typically 3-6 months).

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Robotics, Speech & Audio
    • Host: Chiori Hori
    • Apply Now
  • MS1958: Simulation, Control, and Optimization of Large-Scale Systems

    • MERL is seeking a motivated graduate student to research numerical methods pertaining to the simulation, control, and optimization of large-scale systems. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in numerical methods, control, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 3 months.

    • Research Areas: Control, Multi-Physical Modeling, Optimization
    • Host: Chris Laughman
    • 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.

    • Research Areas: Machine Learning, Multi-Physical Modeling, Optimization
    • Host: Chris Laughman
    • Apply Now
  • OR2105: Preference-based Multi-Objective Bayesian Optimization

    • MERL is looking for a self-motivated and qualified candidate to work on Bayesian Optimization algorithms applied to industrial applications. The ideal candidate is a PhD student with experience and peer-reviewed publications in the general field of derivative-free/zeroth-order optimization, preference will be given to candidates who have contributed to theoretical advances or practical application of Bayesian optimization, especially for multi-objective optimization problems. The ideal candidate will have a strong general understanding of numerical optimization and probabilistic machine learning e.g. Gaussian process regression, and is expected to develop, in collaboration with MERL researchers, state of the art algorithms to optimize parameters for industrial processes or control systems. Proficiency in Python is required. An expected outcome of the internship is one or more peer-reviewed publications. The expected duration is 3-4 months, with flexible starting date.

    • Research Areas: Artificial Intelligence, Machine Learning, Optimization
    • Host: Diego Romeres
    • Apply Now
  • OR2103: Human Robot Collaboration in Assembly Tasks

    • MERL is looking for a self-motivated and qualified candidate to work on human-robot-interaction for manipulation and assembly collaborative scenarios. The ideal candidate is a PhD student and should have experience and records in one or multiple of the following areas. 1) Control, estimation and perception for Robotic manipulation 2) Task and Motion Planning 3) Learning from demonstration algorithms applied to robotic manipulation 4) Machine learning techniques for modeling and control as well as regression and classification problems. 5) Experience in working with robotic systems and familiarity with physics engine simulators like Mujoco, Isaac Gym, PyBullet. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms to solve complex manipulation tasks that involve human and robot collaborations. Proficiency in Python and ROS are required. The expectation is that the research will lead to one or more scientific publications. The expected duration s 3-4 months, with a flexible starting date.

    • Research Areas: Artificial Intelligence, Machine Learning, Robotics
    • Host: Diego Romeres
    • Apply Now