Internship Openings

17 / 55 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. We expect that all internships during 2022 will be in-person at MERL.

It is of course possible that COVID will take a significant turn for the worse in 2022. If that happens, 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, ie by showing your vaccination card.


  • CA1731: Motion planning and control of multi-agent systems

    • MERL is looking for a highly motivated individual to develop planning and control algorithms for multi-agent systems. The internship will also include experimental validation of the proposed algorithms in various robotic testbeds (quadrotors and mini-cars) at MERL. The ideal candidate is experienced in multi-agent motion planning and control, and has successfully demonstrated some of their prior work on hardware testbeds. The candidate must be proficient in ROS and C/C++, and at least familiar with Python and MATLAB. Prior experience with crazyflies and/or hamster robots will be considered a plus. The expected duration of the internship is 3-6 months, and the start date is Summer/Fall 2022.

    • Research Areas: Control, Dynamical Systems, Optimization, Robotics
    • Host: Abraham P. Vinod
    • Apply Now
  • CA1742: Mixed-Integer Programming for Motion Planning and Control

    • MERL is looking for a highly motivated individual to work on tailored computational algorithms and applications of mixed-integer programming for decision making, motion planning and control of hybrid systems. The research will involve the study and development of numerical optimization techniques and/or the implementation and validation of 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: branch-and-bound type methods, heuristics for mixed-integer programming (pre-solve, cutting planes, warm starting, integer-feasible solutions), modeling and formulation of MIPs for hybrid control systems, convex and non-convex optimization, machine learning and real-time optimization. PhD students in engineering or mathematics, especially with a focus on mixed-integer programming or numerical optimization, are encouraged to apply. Publication of relevant results in conference proceedings and 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
  • CA1706: Perception-aware vehicle control

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control algorithms accounting for perception of the uncertain surrounding environment. 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: predictive control algorithms for linear and nonlinear systems, stochastic constrained control, e.g., chance constraints, stochastic optimization, statistical estimation, perception system modeling, and vehicle modeling and control. Good programming skills in MATLAB, Python or C/C++ are required. 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, Optimization, Signal Processing
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1707: Autonomous vehicles guidance and control

    • 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. The research domain includes algorithms for path planning, vehicle control, high level decision making, sensor-based navigation, driver-vehicle interaction. 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, predictive control algorithms linear and nonlinear systems, motion planning, convex, non-convex, and mixed -integer optimization, statistical estimation, cooperative control. Good programming skills in MATLAB, Python or C/C++ are required, knowledge of rapid prototyping systems, automatic code generation or ROS is 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
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1726: Distributed Estimation for Autonomous Systems

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in developing estimation methods with applications to multi-vehicle positioning. The ideal candidate is a PhD candidate with strong emphasis in estimation and control, and as interest and background in several of: bayesian inference, machine learning, maximum-likelihood estimation, optimization, distributed systems, and vehicle modeling and control. Good programming skills in MATLAB, Python, or C/C++ are required. The expected start of of the internship is in 2022 and flexible for a duration of 3-6 months.

    • Research Areas: Control, Optimization, Signal Processing
    • Host: Karl Berntorp
    • Apply Now
  • CA1728: Safe data-driven control of dynamical systems under uncertainty

    • MERL is looking for a highly motivated individual to work on safe control of data-driven, uncertain, dynamical systems. The research will develop novel optimization and learning-based control algorithms to guarantee safety and performance in various industrial applications, including autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: optimal control under uncertainty, (robust and stochastic) model predictive control, (convex and non-convex) optimization, and (reinforcement and statistical) learning. Ph.D. students in engineering or mathematics with a focus on control, optimization, and learning are encouraged to apply. A successful internship will result in submission of relevant results to peer-reviewed conference proceedings and journals, and development of well-documented (Python/MATLAB) code for MERL. The expected duration of the internship is 3-6 months, and the start date is Summer 2022.

    • Research Areas: Artificial Intelligence, Control, Dynamical Systems, Optimization, Robotics
    • Host: Abraham P. Vinod
    • Apply Now
  • MS1717: Estimation and Optimization for Large-Scale Systems

    • MERL is seeking a motivated graduate student to research methods for state and parameter estimation 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 control and estimation, numerical methods, 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
  • MD1697: Integrated design of mechatronic systems

    • MERL is seeking a highly motivated and qualified individual to conduct research in model-based mechatronic system design. The ideal candidate should have solid backgrounds in motor and drives, multi-body dynamics, design optimization, and coding skills. Demonstrated experience on hand-on mechatronic system integration, and simulation/optimization software such as Matlab is a necessity. Ph.D. students in mechanical engineering, robotics, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

    • Research Areas: Multi-Physical Modeling, Optimization, Robotics
    • Host: Yebin Wang
    • Apply Now
  • MD1771: Machine Learning for Electric Machine Design Optimization

    • MERL is seeking a motivated and qualified intern to conduct research on machine learning techniques for design optimization of electrical machines. The ideal candidate should have solid background and demonstrated research experience in mathematical optimization methods, especially in topology optimization and sensitivity analysis, as well as machine learning techniques. Hands-on experiences with the implementation of optimization algorithms, machine learning and deep learning methods are required. Knowledge and experience with electric machine principle, design and finite-element analysis is a strong plus. Senior Ph.D. students in related expertise are encouraged to apply.

    • Research Areas: Artificial Intelligence, Machine Learning, Optimization
    • Host: Bingnan Wang
    • Apply Now
  • MD1761: Blind signal decomposition

    • MERL is seeking a self-motivated intern to work on blind signal decomposition. The ideal candidate would be a senior PhD student with solid background in signal processing, sparse representation, and optimization. Prior experience in array signal processing, compressive sensing, and spectrum analysis is preferred. Skills in Python and/or Matlab are required. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. The expected duration of the internship is 3 months and the start date is flexible. This internship requires work that can only be done at MERL.

    • Research Areas: Computational Sensing, Optimization, Signal Processing
    • Host: Dehong Liu
    • 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
  • SA1778: Metasurface optics design & optimization

    • We are seeking a talented graduate student for a research project in metalens design optimization. Ideal applicants will have a strong mathematics/optimization background, experience with metasurface optics, and fluency in scientific programming. Background knowledge in machine learning, freeform metasurfaces, or polarometry is a plus, as is a working knowledge of any of the following tools: Lumerical, RWCA, ComSol, Matlab, pyTorch/scipy. Creativity and execution are more important than expertise.

    • Research Areas: Applied Physics, Electronic and Photonic Devices, Machine Learning, Optimization
    • Host: Matt Brand
    • Apply Now
  • SP1730: Advanced Signal Processing for RF-controlled metasurface

    • MERL is seeking a highly motivated, qualified intern to carry out research on Advanced Signal Processing for RF-controlled meta-surfaces. The candidate is expected to develop innovative signal processing for RF-controlled meta-surfaces aiding various applications. Candidates should have strong knowledge of machine learning, channel estimation, beamforming, interference mitigation, optimization, and electromagnetic field analysis. Proficient programming skills with Python and MATLAB and strong mathematical analysis will be additional assets to this position. 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, with a flexible start date.

    • Research Areas: Communications, Machine Learning, Optimization, Signal Processing
    • Host: K.J. Kim
    • Apply Now
  • SP1750: THz (Terahertz) Sensing

    • The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in THz (Terahertz) sensing. Expertise in statistical inference, unsupervised anomaly detection, and deep learning (spatial-temporal representation learning) is required. Previous hands-on experience in THz data analysis is a plus. Familiarity with python and deep learning libraries is a must. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with collaborators, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date. This internship requires work that can only be done at MERL.

    • Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Optimization, Signal Processing
    • Host: Perry Wang
    • Apply Now
  • SP1710: Distributed Learning and Computing over Networks

    • MERL is seeking a highly motivated and qualified intern to join the Signal Processing Group for a three month internship program. The ideal candidate will be expected to carry out research on collaborative learning between infrastructure, devices, and vehicles. The candidate is expected to develop distributed learning for various applications, including autonomous driving, smart infrastructure, mobile networks, etc.. The candidate should have knowledge of federated learning and distributed computing, networking and over-the-air-computation. Knowledge of scheduling, spectrum management, and mathematical analysis for convergence testing is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications, Machine Learning, Optimization
    • Host: Jianlin Guo
    • Apply Now
  • SP1747: Learning for Inverse Problems

    • The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that solve inverse problems in computational sensing that incorporate deep learning architectures for a variety of sensing applications. The project goal is to improve the performance and develop an analysis of algorithms used for inverse problems by incorporating new tools from machine learning and artificial intelligence. 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, large-scale optimization, plug-and-play priors, learning-based modeling for imaging, learning theory for computational imaging. 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: Applied Physics, Computational Sensing, Machine Learning, Optimization, Signal Processing
    • Host: Hassan Mansour
    • Apply Now
  • SP1711: Advanced Network Design

    • MERL is seeking a highly motivated and qualified intern to join the Signal Processing Group for a three month internship program. The ideal candidate will be expected to carry out research on network design and optimization methods including AI assisted networking. The candidate is expected to develop innovative network configuration technologies to support emerging IoT applications. The candidates should have knowledge of network technologies such as network slicing, software defined networking and/or semantic networking. Knowledge of the communication technologies such as 3GPP-5G or IEEE 802 WLAN/WPAN standards is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications, Control, Optimization
    • Host: Jianlin Guo
    • Apply Now