Internship Openings

22 / 71 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

As the COVID-19 pandemic continues to evolve, MERL is committed to providing a safe environment for everyone, during these challenging times.

If you believe you meet the qualifications of one of our open internships, please consider applying for the position of interest. A member of the researcher team will follow up to schedule an interview by phone or video conference for qualified candidates.

Effective on August 20, 2021, MERL will require proof of vaccination for any student who is hired and required to work onsite at MERL, during their internship. Please be sure to check for any specific requirements for onsite work in the job description.


  • 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
  • SP1709: Advanced Networking Technologies

    • 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 emerging IoT networking. The candidate is expected to develop innovative networking technologies to achieve efficient network traffic delivery. The candidates should have knowledge of networking protocols such as multi-path TCP/UDP and RPL. Knowledge of the data protection such as erasure coding is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. 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, Electric Systems, Signal Processing
    • Host: Jianlin Guo
    • 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. 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, Robotics
    • Host: Rien Quirynen
    • 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. 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
    • Host: Stefano Di Cairano
    • 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. 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, Dynamical Systems, Optimization, Robotics
    • Host: Abraham P. Vinod
    • Apply Now
  • CA1743: Coordination of Connected and Automated Vehicles

    • MERL is seeking a highly motivated qualified intern to collaborate with the Control for Autonomy team in the development of optimization-based coordination of connected and automated vehicles. The intern will conduct research in the development of methods for multi-vehicle coordination and/or focus on the implementation and validation in realistic scenarios. The ideal candidate should have experience in either one or multiple of the following topics: formulation of mixed-logic constraints as mixed-integer programs, control synthesis from Temporal Logic specifications, connected vehicles and coordination, and vehicle control systems. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) is a plus. 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. 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: Rien Quirynen
    • Apply Now
  • CA1740: Locomotion of Legged Robots

    • MERL is looking for highly motivated interns at different levels of expertise to conduct research on robot 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, machine learning, numerical optimization, and optimal control. Good programming skills in MATLAB, ROS, Python, or C/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. This internship requires work that can only be done at MERL.

    • Research Areas: Control, Machine Learning, Robotics
    • Host: Marcel Menner
    • Apply Now
  • CA1695: Spacecraft Attitude Control

    • MERL is seeking a highly motivated intern for a research position in spacecraft attitude control. The ideal candidate has experience in attitude kinematics and dynamics, computational fluid dynamics (CFD) using OpenFOAM, programming in C++, optimization, and control of rigid bodies and PDEs. Experience in multi-phase flow modeling and volume-of-fluid approach with an emphasis on liquid-gas systems is highly desirable. PhD students in aerospace, mechanical, or electrical engineering are encouraged to apply. Publication of results produced during the internship is expected. The 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, Dynamical Systems, Multi-Physical Modeling
    • Host: Avishai Weiss
    • Apply Now
  • CA1727: Learning for Control

    • MERL is looking for highly motivated interns to work with the Control for Autonomy team in the domain of data-based estimation for integration into control, with applications to, e.g., vehicle control. The ideal candidate is working towards a PhD with emphasis on control and has experience in as many as possible of the following topics: statistical signal processing, Bayesian inference, predictive control, stochastic constrained control, statistical learning. 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 in 2022 but 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, Signal Processing
    • Host: Karl Berntorp
    • Apply Now
  • CA1719: Spacecraft Guidance, Navigation, and Control

    • MERL is seeking highly motivated interns for research positions in guidance, navigation, and control of spacecraft. The ideal candidates have experience in one or more of the following topics: astrodynamics, the three-body problem, relative motion dynamics, rendezvous, attitude control, orbit control, orbit determination, nonlinear estimation, and optimization-based control. PhD students in aerospace, mechanical, or electrical engineering are encouraged to apply. Publication of results produced during the internship is expected. The duration of the internships are 3-6 months, and the start dates are 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
    • Host: Avishai Weiss
    • Apply Now
  • 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. 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: Abraham P. Vinod
    • 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. 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, Optimization, Signal Processing
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1741: Learning for Connected Vehicles

    • MERL is seeking a highly motivated intern to collaborate with the Control for Autonomy team in the development of learning technologies for Connected Vehicles. The intern will conduct research in the development of methods for learning/optimization of Advanced Driver Assistance Systems (ADAS) using data-sharing between connected vehicles and/or infrastructure. The ideal candidate has knowledge of at least one of machine learning, estimation, connected vehicles, and vehicle control systems. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) is a plus. 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. 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
  • 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. 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, Optimization, Signal Processing
    • Host: Karl Berntorp
    • Apply Now
  • CA1705: Fault-tolerant planning and control of autonomous systems

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on fault-tolerant algorithms for 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: invariance and set-based control, decision making, predictive control algorithms for linear and nonlinear systems, formal methods for control, 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. 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, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • DA1702: Machine Learning for Robotic Manipulation

    • MERL is looking for a self-motivated and qualified candidate to work on robotic manipulation projects. The ideal candidate is a PhD student and should have experience and records in one or multiple of the following areas. 1) Machine learning techniques for modeling and control such as Gaussian Processes and Neural Networks. 2) Knowledge of standard Reinforcement Learning algorithms. 3) Experience in working with robotic systems and familiarity with one physics engine simulator like Mujoco, pyBullet, pyDrake. 4) Optimization-based control for complementarity systems. Proficiency in Python is required. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms to solve complex robotic manipulation tasks that will lead to a scientific publication. Typical internship length is 3-4 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, Data Analytics, Dynamical Systems, Optimization, Robotics
    • Host: Diego Romeres
    • 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. 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, Multi-Physical Modeling, Optimization
    • Host: Chris Laughman
    • Apply Now
  • MS1704: Probabilistic Machine Learning for Few-Shot Optimization

    • MERL is looking for a highly motivated and qualified candidate to work on probabilistic machine learning for few-shot optimization with real-world applications in building and energy systems. The ideal candidate will have a strong understanding machine learning with expertise demonstrated via, e.g., publications, in at least one of: few-shot/meta-learning methods, Bayesian optimization, multimodal learning, or learning for control/estimation of buildings and energy systems. Hands-on programming experience with standard ML toolkits such as PyTorch/Tensorflow is required; knowledge of additional, relevant tools (e.g., GPyTorch, Pyro) is a plus. PhD students are 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. 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, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization
    • Host: Ankush Chakrabarty
    • Apply Now
  • MD1736: Data-driven fluid mechanics and control

    • MERL is seeking a highly motivated, qualified individual to join our internship program in the summer of 2022. The ideal candidate will be a senior Ph.D. student specializing in computer science, aerospace, mechanical, or applied mathematics. Research experience in computational fluid dynamics (CFD), C++ (OpenFOAM level), and Python (Keras w/ TensorFlow, PyTorch, etc.) is very desirable. Solid background in two or more of the following areas is required: Physics-Informed Neural Nets (PINNs), adjoint analysis, PDE-constrained optimization, reduced-order modeling (ROMs), statistical learning, parameter estimators, regression techniques, and probability theory. The starting date is flexible, and the internship will last 3-4 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. 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, Multi-Physical Modeling
    • Host: Saleh Nabi
    • Apply Now
  • MD1694: Path Planning for Articulated Vehicles

    • MERL is seeking a highly skilled and self-motivated intern to work on path/motion planning of articulated vehicles.

      The ideal candidate should have solid backgrounds in path/motion planning, nonlinear geometric control theory, and machine learning. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, electrical engineering, robotics, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 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: Control, Machine Learning, Robotics
    • Host: Yebin Wang
    • Apply Now
  • MD1746: PWM inverter circuit design

    • MERL is looking for a self-motivated intern to work on PWM inverter drive circuit design and fabrication. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in power electronics. Experience in PWM inverter design, switching loss estimation, and EMI is desired. The intern is expected to collaborate with MERL researchers to design, simulate, and fabricate circuits, carry out experiments, analyze experimental data, and prepare manuscripts for scientific publications. The total duration is 3 months. This internship requires work that can only be done at MERL. This internship requires work that can only be done at MERL.

    • Research Areas: Control, Electric Systems, 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