Internship Openings

10 / 29 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, Sudan 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

The COVID pandemic has impacted every aspect of life-how we live, work, and interact. At MERL, we are committed to maintaining our internship program through these challenging times.

MERL continues to actively seek candidates for research internships -- some of the posted positions are immediately available, while others target the summer of 2021. Please consider applying for positions of interest. Our researchers will follow up to schedule an interview by phone or video conference for qualified candidates.

Due to the situation with the COVID-19 pandemic, our current internships are mostly remote. Next summer we hope the situation will be better and our internships will be at MERL, but if it is not, most internships will continue to be remote. However, some of the internships require onsite work. Please check for any specific requirements for onsite work in the job description.


  • DA1602: Reinforcement Learning for HVAC systems

    • MERL is looking for a self-motivated and qualified candidate to work on air flow control of Heating, Ventilation and Air Conditioning (HVAC) systems. The ideal candidate is a PhD student and should have experience and records in multiple of the following areas: fluid dynamics, control theory, reinforcement learning, familiarity with partial differential equations. Proficiency in Python and Matlab is required. The successful candidate will be expected to develop, in collaboration with MERL employees, a state of the art algorithms for air flow control that will lead to a scientific publication. Typical internship length is 3 months. 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: Applied Physics, Control, Machine Learning
    • Host: Diego Romeres
    • Apply Now
  • CA1519: Estimation for High-Precision Positioning

    • MERL is seeking a highly motivated candidate for development of next-generation high-precision positioning methods for autonomous systems applications, e.g., autonomous driving. The candidate will work with the Control for Autonomy team and the Signal Processing group in developing satellite-based positioning methods using information from multiple sources. Previous experience with at least some of the Bayesian inference, distributed estimation, satellite navigation systems, is highly desirable. Solid knowledge in MATLAB is required, working experience in C/C++ is desired, and previous experience with satellite navigation packages such as RTKLib is a merit. PhD candidates meeting the above requirements are encouraged to apply. The expected duration of the internship is 3-6 months with flexible start date. 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: Control, Machine Learning, Signal Processing
    • Host: Karl Berntorp
    • Apply Now
  • CA1529: Energy Management for Electric Vehicles

    • MERL is looking for a highly motivated intern to conduct research on data-driven energy management strategies for (hybrid) electric vehicles. The candidate will develop methods that use data, e.g., of human drivers or traffic conditions, in order to improve the control of electric vehicles. The ideal candidate will have experience in either one or multiple of the following topics: model predictive control, machine learning, statistical learning, numerical optimization, and (inverse) optimal control. Prior experience with (hybrid) electric vehicles is a plus. Good programming skills in MATLAB, Python, or C/C++ are required. PhD students in engineering or mathematics with a focus on 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. 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: Control, Dynamical Systems, Machine Learning
    • Host: Marcel Menner
    • Apply Now
  • CA1565: Connected Vehicle Driver Assistance Systems

    • MERL is seeking a highly motivated qualified intern to collaborate with the Control for Autonomy team and the Signal Processing group in the development of Advanced Driver Assistance Systems (ADAS) for Connected Vehicles. The intern will collaborate in the development of methods for distributed learning and optimization of ADAS using data-sharing between connected vehicles and infrastructure. The ideal candidate has knowledge of machine learning, optimization and connected vehicles. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) is a plus. Good programming skills in MATLAB, Python, or C/C++ are required. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. The expected duration of the internship is 3-6 months, starting in Spring or Summer 2021, but later starting periods may also be considered. 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: Control, Machine Learning, Optimization
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1528: Learning-Based Stochastic Predictive Control

    • MERL is looking for highly motivated interns to work in the domain of data-based controller design and algorithms for stochastic model predictive control (MPC) methods. The research involves the derivation, implementation, and validation of novel algorithms for optimization-based/data-driven control for industrial applications, e.g., related to autonomous driving and robotics. The ideal candidate has experience in either one or multiple of the following topics: stochastic MPC (e.g., scenario trees or tube MPC), (inverse) optimal control, convex and nonconvex optimization, parallel processing, real-time optimization, machine learning, statistical learning, and Bayesian inference. PhD students in engineering or mathematics, especially with a focus on stochastic and learning-based control or numerical optimization, 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. 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: Control, Machine Learning, Optimization
    • Host: Karl Berntorp
    • Apply Now
  • CA1530: Hybrid Control of Cyberphysical Systems

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of hybrid control algorithms for cyberphysical system. The potential subjects include formal methods for control synthesis, control barrier-functions, stabilizing control for hybrid dynamical systems, and optimal control of hybrid dynamics. The ideal candidate is expected to be working towards a PhD with strong emphasis in control theory, and to have interest and background in as many as possible among: predictive control, Lyapunov stability, formal methods for control, constrained control, optimization, and machine learning. Good programming skills in MATLAB, and/or Python are required. The expected duration of the internship is in the Spring of 2021, for a duration of 3-6 months. 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: Control, Dynamical Systems, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1531: Learning-based multi-agent motion planning

    • MERL is seeking a highly motivated intern to research multi-agent motion planning by combining optimization-based methods with machine learning. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in multi-agent motion planning, machine learning (especially supervised, reinforcement, and safe ML), and convex and non-convex optimization. A successful internship will result in innovative methods for multiagent planning, in the development of well-documented (Python/MATLAB) code for validating the proposed methods, and in the submission of relevant results for publication in peer-reviewed conference proceedings and journals. The expected duration of the internship is 3 months with a flexible start date in the Spring/Summer 2021. 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: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Abraham P. Vinod
    • Apply Now
  • CA1520: Autonomous Vehicles: Perception, Planning, and Control

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of algorithms for planning and control of autonomous vehicles. The potential subjects include high level decision making using formal methods and set-based control, coordination or perception and control strategies to improve environment knowledge while achieving a goal, and distributed control for multi-vehicle systems. 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 among: motion planning, predictive control, perception and object detection optimization, machine learning for vehicle prediction, autonomous vehicles. Good programming skills in MATLAB, Python or C/C++ are required. The expected duration of the internship is in the Spring of 2021, for a duration of 3-6 months. 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: Artificial Intelligence, Control, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • MD1377: Adaptive Optimal Control of Electrical Machines

    • MERL is seeking a motivated and qualified individual to conduct research in control of electrical machines. The ideal candidate should have solid backgrounds in adaptive dynamic programming and state/parameter 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 flexible and the duration is about 3 months.

    • Research Areas: Control, Electric Systems, Machine Learning
    • Host: Yebin Wang
    • Apply Now
  • MD1300: 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: Abraham Goldsmith
    • Apply Now