Internship Openings

21 / 70 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.


  • CI1948: 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, 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
  • CV1902: Human-Robot Interaction for Robotic Manipulation

    • MERL is looking for a highly motivated and qualified intern to work on human-robot interaction (HRI) research. The ideal candidate would be a Ph.D. student with a strong background in HRI, focusing on robotic manipulation, deep learning, probabilistic modeling, or reinforcement learning. Several topics are available for consideration, including Intent Recognition, Shared Autonomy, Human-Robot Teaming for Automation /Manufacturing, Human-Robot Handover, Computer Vision for HRI, and Learning for Robot Programming. Experience working with robotics hardware and physics engine simulators like PyBullet, Issac Gym, Mujoco, or Gazebo is preferred. Proficiency in Python programming is necessary, and experience with ROS is a plus. The successful candidate will collaborate with MERL researchers, and publication of the relevant results is expected. The start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics.

    • Research Areas: Computer Vision, Machine Learning, Robotics
    • Host: Siddarth Jain
    • Apply Now
  • CV1930: Meta-Algorithmic Learning for Vision-based Robotic Manipulation

    • MERL is looking for a self-motivated intern to work on problems at the intersection of computer vision and robotic manipulation for solving tasks such as vision-based robotic tool manipulation. The ideal candidate would be a PhD student with strong mathematical background in machine learning/reinforcement learning, modeling contact-physics for object manipulation, and experience in working with and training deep models on large scale computer vision datasets. Proficiency in PyTorch and (differentiable) robotic simulators is expected. Knowledge of meta-learning, hierarchical RL, self-supervised learning, and scene graph based visual reasoning would be useful. The intern will conduct original research with MERL researchers towards scientific publications.

    • Research Areas: Artificial Intelligence, Computer Vision, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Anoop Cherian
    • Apply Now
  • CV1912: Multimodal Embodied AI

    • MERL is looking for a self-motivated intern to work on problems at the intersection of visual understanding, audio processing, language models, and embodied navigation AI (see our recent NeurIPS 2022 paper for the context). The ideal candidate would be a senior PhD student with a strong background in machine learning and computer vision, as demonstrated by top-tier publications. The candidate must have prior experience in developing deep learning methods for audio-visual-language data. Expertise in popular embodied AI simulation environments as well as a strong background in reinforcement learning will be beneficial. The intern is expected to collaborate with researchers in computer vision and speech teams at MERL to develop algorithms and prepare manuscripts for scientific publications.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Robotics, Signal Processing
    • Host: Anoop Cherian
    • Apply Now
  • CV1938: Component transfer learning for RL and robotic applications

    • MERL is offering a new research internship opportunity in the field of Transfer Learning for Deep RL. The position requires a strong background in Deep RL, excellent programming skills and experience with robotics is preferred. The position is open to graduate students on a PhD track only, and the length of the internship is three months with the possibility of extending if required. The intern is expected to disseminate this research in top tier scientific conferences such as RSS, IROS, ICRA etc., and if applicable, help with filing associated patents. Start and end dates are flexible.

    • Research Areas: Artificial Intelligence, Machine Learning, Robotics
    • Host: Radu Corcodel
    • Apply Now
  • CV1901: Deep Learning for Robotic Grasping

    • MERL is looking for a highly motivated and qualified intern to work on computer vision for visual feedback and robotic grasping. The ideal candidate would be a Ph.D. student with a strong background in deep learning and robotic manipulation. Several topics are available for consideration, including state estimation of objects at high speed, closed-loop reactive grasping, vision-guided dynamic object grasping, goal-driven grasping, deformable object grasping, and grasping in clutter. The project requires the development of novel algorithms with implementation and evaluation on a robotic platform. Experience working with a physics engine simulator like PyBullet, Issac Gym, Mujoco, or Gazebo is preferred. Proficiency in Python programming is necessary, and experience with ROS is a plus. The successful candidate will collaborate with MERL researchers, and publication of the relevant results is expected. The start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics.

    • Research Areas: Computer Vision, Machine Learning, Robotics
    • Host: Siddarth Jain
    • Apply Now
  • MD1886: Co-design of robotic arm and control systems

    • MERL is seeking a highly motivated and qualified individual to conduct research in model-based robotic system design. The ideal candidate should have solid backgrounds in robotic dynamics and simulation, motion planning and control, simulation-based optimization, surrogate modeling, and coding skills. Demonstrated experience on implementing robotic dynamics and simulation/optimization software such as Matlab is a necessity. Ph.D. students in mechanical engineering, robotics, computer science, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

    • Research Areas: Control, Dynamical Systems, Optimization, Robotics
    • Host: Yebin Wang
    • Apply Now
  • DA1895: Human Robot Interaction

    • MERL is looking for a self-motivated and qualified candidate to work on human-robot-interaction projects. 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) Experience in shared autonomy between robot and humans and intent recognition 3) Learning from demonstration algorithms applied to robotic manipulators 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 one physics engine simulator like Mujoco, pyBullet, pyDrake. 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 robot collaboration. Exceptional programming skills are required, including Python and ROS. The expectation is that the research will lead to one or more scientific publications. Typical internship length is 3-4 months.

    • Research Areas: Artificial Intelligence, Control, Machine Learning, Robotics
    • Host: Diego Romeres
    • Apply Now
  • DA1960: Unconventional robotic manipulation

    • This internship will be in the domain of robotic manipulation beyond the conventional methods such as 2-jawed grippers without feedback, mounted on a 6-DoF robot arm. The ideal candidate would have a wide mechatronic background as well as some familiarity with machine learning and machine vision, strong programming skills in Python and C on Linux and embedded systems (especially Arduino), CAD skills in FreeCAD, OpenSCAD, and KiCAD for mechatronic design, and hands-on hardware prototyping skills (i.e. CNC, 3D printing, soldering). A wild imagination is a definite plus.

    • Research Areas: Data Analytics, Machine Learning, Robotics
    • Host: Bill Yerazunis
    • Apply Now
  • DA1935: Robot Learning Algorithms

    • MERL is looking for a highly motivated and qualified PhD student in the areas of machine learning and robotics, to participate in research on advanced algorithms for learning control of robots and other mechanisms. Solid background and hands-on experience with various machine learning algorithms is expected, and in particular with deep learning algorithms for image processing and object detection. Exposure to deep reinforcement learning and/or learning from demonstration is highly desirable. Familiarity with the use of machine learning algorithms for system identification of mechanical systems would be a plus, along with background in other areas of automatic control. Solid experimental skills and hands-on experience in coding in Python and PyTorch are required for the position. Some familiarity with classical mechanics and computational physics engines would be helpful, but is not required. The position will provide opportunities for exploring fundamental problems in incremental learning in humans and machines, leading to publishable results. The starting date of the internship is flexible, and applications outside of the peak summer season are encouraged, too.

    • Research Areas: Artificial Intelligence, Computer Vision, Control, Machine Learning, Robotics
    • Host: Daniel Nikovski
    • Apply Now
  • DA1931: Optimization Algorithms

    • MERL is seeking highly motivated intern to work on the development of novel optimization algorithms. The target applications span a broad range of areas including power systems, control, scheduling, and transportation. Successful candidate will collaborate with MERL researchers to develop and implement new algorithms, conduct experiments, and prepare results for publication. Ideal candidate would be senior PhD student with experience in one or more of the following areas: linear and quadratic programming, active-set methods, and first-order methods for convex programs. Strong programming skills and fluency in C++/Python are expected. Prior experience with popular optimization packages such as Ipopt, Gurobi, Cplex is a plus. The duration of the internship is expected to be 3 months. Start date is flexible. 

    • Research Areas: Control, Optimization, Robotics
    • Host: Arvind Raghunathan
    • Apply Now
  • DA1926: Robotic Manipulation Control using VisuoTactile Sensing

    • MERL is looking for a highly motivated individual to work on robust, closed-loop control of robotic manipulation system using vision and tactile feedback. The research will develop novel optimization and control techniques that can be used for closed-loop control of manipulation systems. The ideal candidate should have experience in either one or multiple of the following topics: optimization for contact-rich systems, stochastic optimization of non-linear systems, stochastic model-predictive control and reinforcement learning. Senior PhD students in robotics and engineering with a focus on contact-rich manipulation are encouraged to apply. Prior experience working with physical robotic systems (and vision & tactile sensors) is required as results need to be implemented on physical hardware. A successful internship will result in submission of results to peer-reviewed conferences and journals. Good coding skills in Python and state-of-the-art optimization packages like IPOPT, SNOPT, etc. is required. The expected duration of internship is 3-4 months with start date in May/June 2023. This internship is preferred to be onsite at MERL.

    • Research Areas: Artificial Intelligence, Control, Machine Learning, Optimization, Robotics
    • Host: Devesh Jha
    • Apply Now
  • CA1905: Coordination and Control of Connected Autonomous Vehicles

    • MERL is looking for a highly motivated individual to work on optimization-based techniques for coordination and control of connected autonomous vehicles (CAVs), in the presence of other CAVs and human driven vehicles (HDVs). The research will involve the development, implementation, and validation of optimization-based coordinated control of vehicles through traffic intersections and/or merging scenarios. The ideal candidate should have experience in either one or multiple of the following topics: vehicle modeling and/or traffic modeling, mixed-integer programming, (stochastic) model predictive control, reinforcement learning, data-driven (e.g., Gaussian Process) modeling, hybrid dynamical systems, coordination and control of CAVs. Knowledge of one or multiple vehicle and/or traffic simulators (SUMO, CARLA, CarSim, Vissim, etc.) is a plus. Publication of relevant results in conference proceedings or journals is expected. 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 and the start date is flexible.

    • Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Rien Quirynen
    • Apply Now
  • CA1939: Motion Planning, Estimation and Control for Articulated Vehicles

    • MERL is seeking a highly motivated and qualified intern to collaborate with multiple researchers on the improvement, real-time implementation and experimental validation of algorithms for path/motion planning, constrained state estimation, optimal control and reference tracking in autonomous articulated vehicles. The ideal candidate should have a background in either path/motion planning, state and parameter estimation and/or model predictive control (MPC) for autonomous (articulated) vehicles, and the candidate should have experience in one or multiple of the following topics: optimal control, MPC, vehicle dynamics, A* search, RRT, Kalman filtering, particle filtering, and machine learning. Capability of implementing the designs and algorithms in Matlab and Simulink, and using C/C++ code generation is expected. Any experience with dSPACE (e.g., MicroAutoBox or Scalexio), CasADi, and/or experience with vehicle experiments or simulators (e.g., TruckSim or CarSim) is a plus. Publication of relevant results in conference proceedings or journals is expected. MS or PhD students in control, robotics, electrical and mechanical, or related areas, are encouraged to apply. The expected duration of the internship is 3-6 months and the start date is flexible.

    • Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Rien Quirynen
    • Apply Now
  • CA1928: Multi-agent systems for resource monitoring

    • MERL is looking for a highly motivated individual to develop planning and control algorithms for multi-agent systems for resource monitoring. The ideal candidate has experience in multi-agent motion planning and data-driven sequential decision-making. The ideal candidate will have published in one or more of these topics: planning over discrete spaces, task scheduling and assignment, vehicle routing and scheduling problems, multi-arm bandits, reinforcement learning, and planning and control of aerial and ground robots. The candidate should have a working knowledge of ROS and Python/C++ since the internship will include validation in various simulation/hardware testbeds at MERL. The minimum duration of the internship is 3 months; the start time is Summer/Fall 2023.

    • Research Areas: Artificial Intelligence, Control, Machine Learning, Optimization, Robotics
    • Host: Abraham Vinod
    • Apply Now
  • CA1941: Risk-aware fault tolerant control of autonomous vehicles

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on risk-aware, fault-tolerant planning and control of autonomous vehicles. The ideal candidate is expected to be working towards a PhD with strong emphasis in control or planning algorithms, and to have interest and background in as many as possible of: physics-based and data-based prediction models, formal methods for probabilistic validation, invariance and set-based control, predictive control algorithms for linear and nonlinear systems and vehicle modeling and control. Good programming skills in MATLAB, Python are required. Knowledge of ROS and C/C++ 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
  • CA1954: Control and Motion Planning for Quadrotors

    • MERL is seeking a highly motivated and qualified intern to work on fundamental algorithms for motion planning and control of multiple autonomous quadrotor aerial vehicles. The ideal candidate should have a background in nonlinear control, estimation theory, and applied optimization. The candidate should have experience in one or multiple of the following topics: optimal control, Lyapunov stability theory, quadrotor dynamics, Kalman filtering, particle filtering, and machine learning. Capability of implementing the designs and algorithms in Matlab and Simulink is expected, and experience with platforms such as the Crazyflie is a plus. Publication of relevant results in conference proceedings or journals is expected. MS or PhD students in control, robotics, electrical engineering, computer science, or related areas, are encouraged to apply. The expected duration of the internship is 3-6 months and the start date is flexible.

    • Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Marcus Greiff
    • Apply Now
  • CA1942: Model predictive control for system with perception uncertainty

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on design and analysis of model predictive control algorithms for systems subject to environment uncertainty that can be reduced by perception. The research domain includes algorithms for stabilizing uncertain and stochastic model predictive control, uncertainty quantification and reduction via estimation, optimization algorithms for uncertain and stochastic predictive control. The ideal candidate is expected to be working towards a PhD with strong emphasis in some of: stochastic model predictive control, statistical estimation, uncertainty quantification, and sensing-driven control. Good programming skills in MATLAB, Python are required, knowledge of C/C++ 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
  • CA1904: Numerical Optimal Control for Hybrid Dynamical Systems

    • MERL is looking for a highly motivated individual to work on tailored computational algorithms for numerical optimal control of hybrid dynamical systems and applications for decision making, motion planning and control of autonomous systems. The research will involve the study and development of numerical optimal control methods for systems with continuous dynamics and discrete logic, nonsmooth and/or switched dynamics, and the implementation and validation of such 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: mixed-integer programming (MIP), mathematical programs with complementarity constraints (MPCCs), modeling and formulation of optimal control problems for hybrid dynamical systems, convex and non-convex optimization, machine learning and real-time optimization. PhD students in engineering or mathematics, especially with a focus on MIPs, MPCCs or numerical optimal control, 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 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
  • 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
  • CA1923: Locomotion of Legged Robots

    • MERL is looking for an intern to conduct research on locomotion of legged robots. The research spans multiple areas from modeling, motion planning, sensing and learning from data, to control. The ideal candidate will have experience in either one or multiple of the following topics: model predictive control, statistical estimation, machine learning, numerical optimization, control theory, and reinforcement learning. Good programming skills in MATLAB, ROS, Python, or C++ are required. Graduate students in robotics, engineering, or mathematics with a focus on legged robots, control theory, or numerical optimization are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. The expected duration of the internship is 3-6 months. The start date is flexible.

    • Research Areas: Control, Optimization, Robotics
    • Host: Marcel Menner
    • Apply Now