Internship Openings

11 / 62 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).


  • CD1261: Dynamics and Control of Electric Vehicles

    • MERL is seeking a motivated and qualified individual to conduct research on dynamics and control of electric vehicles. The ideal candidate should have a solid background in vehicle dynamics and control, model predictive control, and/or optimization. PhD students in mechanical or electrical engineering with focus on automotive systems are encouraged to apply. The intern will collaborate with MERL researchers in developing nonlinear MPC algorithms for electric vehicles. Start date for this internship is flexible and the expected duration is approximately 3-6 months.

    • Research Areas: Control, Dynamical Systems
    • Host: Claus Danielson
    • Apply Now
  • CD1295: Modeling and data-assimilation of atmospheric flows

  • 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
  • CD1256: Reinforcement learning for dynamical systems

    • MERL is seeking a highly motivated individual for collaboration on reinforcement learning with application to large-scale multi-agent systems. The successful candidate will be an advanced graduate student with extensive knowledge of reinforcement learning, preferably with experience in implementation, and preferably with knowledge of dynamics and control of large-scale systems.

    • Research Areas: Control, Dynamical Systems, Machine Learning
    • Host: Uroš Kalabić
    • Apply Now
  • CD1270: Numerical Optimization Algorithms for Predictive Control

    • MERL is looking for a highly motivated individual to work on efficient numerical algorithms and applications of optimization based control methods. The research will involve the study and development of novel optimization techniques for predictive control and estimation and/or the implementation and validation of algorithms for industrial applications. The ideal candidate should have experience in either one or multiple of the following topics: convex and non-convex optimization, Newton-type optimization algorithms, numerical optimization (e.g. active-set or interior point) and optimal control. PhD students in engineering or mathematics with a focus on numerical optimization or numerical optimal control are encouraged to apply. Publication of results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab is required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is roughly 3 months and the start date is flexible.

    • Research Areas: Control, Dynamical Systems, Optimization
    • Host: Rien Quirynen
    • 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
  • CD1258: Algorithms for GNSS localization

    • The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to work on GNSS Positioning and localization. Previous experience with at least some of the GNSSs, particle filtering, interacting multiple model filtering, Kalman-type filtering, and ambiguity resolution is highly desirable. Working knowledge of C/C++ is required, and previous experience with GNSS packages such as RTKLib is highly desired. PhD and senior Master students meeting the above requirements are welcome to apply. The expected duration of the internship is 3-6 months with flexible start date.

    • Research Areas: Control, Dynamical Systems, Signal Processing
    • Host: Stefano Di Cairano
    • Apply Now
  • CD1298: Theoretical and computational aspects of mean-field control

    • We are looking for a graduate student intern to work on theoretical and computational aspects of mean-field control and mean-field games. An ideal candidate will be a graduate student working on MFC/MFG or Optimal Transport. Expertise in TWO or more of the following areas is required: 1). Optimal control 2). Control of PDEs 3). Geometric methods of dynamical systems theory 4). Statistical Mechanics 5). Stochastic analysis 6). Optimal Transport. Ph.D. students from top programs in engineering, physics, applied math are encouraged to apply. The duration of the internship will be 3-6 months. Publication of results is highly encouraged.

    • Research Areas: Applied Physics, Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Piyush Grover
    • Apply Now
  • CD1296: Optimization and Control of Thermo-fluid Systems

  • CD1247: Control of Space Vehicles

    • MERL's Mechatronics group is seeking a highly motivated intern for a research position in control of space vehicles. The ideal candidate is working towards a Ph.D. in aerospace, mechanical, or electrical engineering, and has background in both optimization-based control and space vehicle dynamics. The candidate is expected to possess strong abilities in algorithm analysis and Matlab implementation. The duration of the internship is approximately 3 months. Publication of results produced during the internship is expected.

    • Research Areas: Control, Dynamical Systems, Optimization
    • Host: Avishai Weiss
    • 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