Internship Openings

6 / 13 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.

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.

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.

  • 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
  • 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
  • CA2028: Mobile robots sensing, planning, and control

    • MERL is seeking a highly motivated intern to collaborate in the development and experimental validation of sensing, planning, and control methods in various robotic testbeds (quadrotors, turtlebots, and mini-cars) 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 successful candidate is proficient in ROS, C/C++, and Python, and at least familiar with MATLAB. The expected duration of the internship is 6 months with a flexible start date in the late Fall/Winter 2023.

    • Research Areas: Control, Dynamical Systems, Robotics
    • Host: Abraham Vinod
    • Apply Now
  • CA1944: Vehicle Estimation and Learning

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on GNSS-based estimation and sensor fusion algorithms. The ideal candidate is expected to be working towards a PhD with strong emphasis in statistical signal processing and estimation, and with experience in at least some of the following areas: application and theory of Bayesian inference, learning, Kalman filters, variational Bayes, automotive, autonomous vehicles, distributed estimation, large-scale estimation, sensor fusion including camera radar and/or GNSS. Good programming skills in MATLAB, Python, or C/C++ are required. The expected duration of the internship is 3-6 months.

    • Research Areas: Control, Signal Processing
    • Host: Karl Berntorp
    • Apply Now
  • MS2012: Residual Model Learning for Building Energy Systems

    • MERL is looking for a highly motivated and qualified candidate to work on learning residual dynamics to augment ODE/DAE-based models of building energy systems. The ideal candidate will have a strong understanding of system identification, optimization, machine learning and/or function approximation; additional understanding of energy systems is a plus. Hands-on programming experience with numerical optimization solvers and Python is preferred; experience with Modelica/FMUs is a plus. PhD students are strongly preferred, as an expected outcome of the internship is a publication in a high-tier venue. The minimum duration of the internship is 12 weeks; start time is flexible.

    • Research Areas: Control, Machine Learning, Multi-Physical Modeling, Optimization
    • Host: Ankush Chakrabarty
    • 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