Internship Openings

26 Intern positions are currently open.

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).


  • SP1307: Vehicular traffic environment sensing

    • MERL is seeking a highly motivated, qualified intern to join a three month internship program. The ideal candidate will be expected to carry out research on environmental sensing in high frequency bands. The candidate is expected to develop innovative sensing technologies. Candidates should have strong knowledge about neural network and learning techniques, such as machine learning, deep learning, shallow learning, and distributed learning. In addition, understanding of spectrum sensing and wireless communications technologies is necessary. Proficient programming skills with Python, Matlab, and C++, 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.

    • Research Areas: Signal Processing
    • Host: K.J. Kim
    • Apply Now
  • SP1441: Advanced Phased Array Transceiver

    • MERL is looking for a highly motivated, and qualified individual to join our internship program of advanced phased array research. The ideal candidate should be a senior Ph.D. student with rich experience in beam forming technologies. Knowledge of wireless communication, transceiver architecture, and digital signal processing, FPGA and/or Matlab programming skills are required. RF circuits knowledge will be a plus. Duration is 3-6 months with a flexible start date.

    • Research Areas: Artificial Intelligence, Communications, Electronic and Photonic Devices, Signal Processing
    • Host: Rui Ma
    • Apply Now
  • SP1155: Coexistence of the Heterogeneous Wireless Technologies

    • MERL is seeking a highly motivated, qualified intern to join the Electronics and Communications group for a three month internship program. The ideal candidate will be expected to carry out research on coexistence of the heterogeneous wireless technologies in the Sub-1 GHz (S1G) band. The candidate is expected to develop innovative coexistence technology for IEEE 802.15.4g to mitigate interference caused by other S1G technologies such as IEEE 802.11ah, LoRa and SigFox. The candidates should have knowledge of 802.15.4g and 802.11ah protocols. Additionally, the candidate should also be familiar with NS3 simulators. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications, Signal Processing
    • Host: Jianlin Guo
    • Apply Now
  • SP1410: Coherent Optical Sensing

    • MERL is seeking an intern to work on coherent optical sensing. The ideal candidate would be an experienced PhD student or post-graduate researcher working in coherent sensing. The candidate should have a detailed knowledge of optical interferometry and imaging with a focus on either optical coherence tomography, optical coherence microscopy or FMCW LIDAR. Strong programming skills in Matlab are essential. Experience of working in an optical lab environment would be advantageous. Duration is 3 to 6 months.

    • Research Areas: Computational Sensing, Signal Processing
    • Host: David Millar
    • Apply Now
  • SP1370: Machine Learning based DPD for Power Amplifier

    • MERL is looking for a talented intern to work on the next generation Digital-predistortion algorithms for power amplifier linearization such as 5G. The development of a DPD system involves aspects of signal processing and statistical algorithm design, RF components and instrumentation, digital hardware and software. It is therefore both a challenging and intellectually rewarding experience. This will involve MATLAB coding, interfacing to test equipment such as power sources, signal generators and analyzers and construction and calibration of RF component assemblies. The ideal candidate should have knowledge and experience in adaptive signal processing, machine learning, and radio communication. Good practical laboratory skills are needed. RF semiconductor devices and circuit knowledge is a plus. Duration is 3 to 6 months.

    • Research Areas: Communications, Electronic and Photonic Devices, Machine Learning, Signal Processing
    • Host: Rui Ma
    • Apply Now
  • SP1409: Coherent optical transmission systems

    • MERL is seeking an intern to work on systems and subsystems for coherent optical fiber transmission. The ideal candidate would be an experienced PhD student or post-graduate researcher working in optical communications. The candidate should have a detailed knowledge of optical communications systems at the physical layer and digital signal processing for digital coherent communication, with a focus on optical fiber communication. Strong programming skills in Matlab are essential. Experience of working in a lab environment would be advantageous. Duration is 3 to 6 months.

    • Research Areas: Communications, Signal Processing
    • Host: Kieran Parsons
    • Apply Now
  • SP1398: Electrical machine modeling

    • MERL is looking for a self-motivated intern to work on electrical machine modelling and signal processing. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in electrical machines, signal processing, and electrical circuit analysis. Experience in transient analysis of electrical machines is desirable. Proficiency in MATLAB and simulink is necessary. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. The total duration is 3 months.

    • Research Areas: Computational Sensing, Electric Systems, Signal Processing
    • Host: Dehong Liu
    • Apply Now
  • SP1419: Simulation of Multimodal Sensors

    • MERL is seeking a motivated intern to assist in generating simulated multimodal data for machine learning applications. The project involves integrating several existing software components to generate optical and radar data in a variety of sensing scenarios, and executing the simulations under a variety of conditions. The ideal candidate should have experience with C++, Python, and scripting methods. Some knowledge or experience with Blender, computer graphics, and computer vision would be preferred, but is not required. Project duration is flexible in the range of 1-2 months. Intern has the choice of part-time or full-time occupation and may start immediately.

    • Research Areas: Artificial Intelligence, Computer Vision, Signal Processing
    • Host: Petros Boufounos
    • Apply Now
  • SP1424: Advanced computational sensing technologies

    • The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop computational imaging algorithms for a variety of sensing applications. 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, learning for inverse problems, large-scale optimization, blind inverse scattering, radar/lidar/sonar imaging, or wave-based inversion. Experience with experimentally measured data is desirable. 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: Artificial Intelligence, Computational Sensing, Signal Processing
    • Host: Petros Boufounos
    • Apply Now
  • SA1031: Distributed auctions for network welfare maximization

    • We are looking for a talented individual to collaborate and facilitate research on new algorithms in mechanism design and distributed auctions. Responsibilities will include mathematical modeling, algorithm design, software prototyping, and running Monte Carlo simulations in a network traffic domain. Candidates should be strong scientific programmers and have some background in numerical optimization, simulation design, and auction theory.

    • Research Areas: Optimization
    • Host: Matt Brand
    • Apply Now
  • MP1263: Fault analysis for electric motors

    • MERL is seeking a highly motivated intern to conduct research in electric machine fault analysis. The ideal candidate should be a senior Ph. D student in Electrical Engineering or related discipline with a solid background in the physics and engineering of electric motors, and early fault detection. Knowledge and experience in electric motor modeling and machine learning are desired. The candidate is expected to collaborate with MERL researchers to conduct theoretical analysis, numerical simulations, develop algorithms and prepare manuscripts for scientific publications. The duration of internship is expected to be 3 months and start date is flexible.

    • Research Areas: Applied Physics, Electric Systems, Multi-Physical Modeling
    • Host: Bingnan Wang
    • Apply Now
  • MP1443: Numerical optimization of thermal fluid systems

    • MERL is seeking a motivated and qualified individual to conduct research in numerical modeling and optimization of thermal fluid systems. A solid background in numerical simulation of fluid dynamics and heat transfer is required. Skills in numerical optimization in partial differential equations are expected and experiences with OpenFOAM are highly desirable. Senior Ph.D. students in applied mathematics, chemical/mechanical engineering and other related areas are encouraged to apply. The duration of the internship is about 3 months.

    • Research Areas: Multi-Physical Modeling
    • Host: Hongtao Qiao
    • Apply Now
  • MP1406: Numerical Analysis of Electric Machines

    • MERL is seeking a motivated and qualified intern to conduct research in the design, modeling and optimization of electrical machines. The ideal candidate should have solid backgrounds in electromagnetic theory, electric machine design, and numerical modeling techniques (including model reduction), research experiences in electric, magnetic, and thermal modeling and analysis of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and optimization techniques is a strong plus. Senior Ph.D. students in electrical or mechanical engineering with related expertise are encouraged to apply. Start date for this internship is flexible and the duration is 3-6 months.

    • Research Areas: Dynamical Systems, Multi-Physical Modeling, Optimization
    • Host: Bingnan Wang
    • Apply Now
  • MP1381: Electric Motor Design

    • MERL is seeking a motivated and qualified individual to conduct research in design, modeling, and simulation of electrical machines. The ideal candidate should have solid backgrounds in modeling (including model reduction)/co-simulation of electromagnetics and thermal dynamics of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and real-time control experiments involving motor drives is a strong plus. Senior Ph.D. students in electrical or mechanical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3-6 months.

    • Research Areas: Applied Physics, Electric Systems, Multi-Physical Modeling
    • Host: Bingnan Wang
    • Apply Now
  • CV1422: Generative Adversarial Networks (GANs)

    • Generative adversarial networks (GANs) and related methods have generated much excitement for their ability to synthesize images and data that appear remarkably realistic. MERL is seeking a highly motivated intern to conduct original research in the area of generative adversarial networks. The successful candidate will collaborate with MERL researchers to design and implement new models, conduct experiments, and prepare results for publication. The candidate should be a PhD student in computer vision with experience in GANs and related deep learning methods, as well as good general knowledge in machine learning and a strong publication record. Strong programming skills in Python, flexibility working across various deep learning platforms (e.g., PyTorch and TensorFlow), and previous experience coding GANs are expected.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Tim Marks
    • Apply Now
  • CV1421: Uncertainty Estimation in Deep Landmark Localization

    • While deep networks have been highly successful at visual regression problems such as face landmark estimation and human body tracking, relatively little work has been done on estimating the uncertainty of their predictions. We are seeking a highly motivated intern to conduct original research in the area of uncertainty estimation for deep network predictions. The successful candidate will collaborate with MERL researchers to design and implement new models, conduct experiments, and prepare results for publication. The candidate should be a PhD student in computer vision and machine learning with a strong publication record and experience in deep learning-based face or body landmark estimation and tracking. Strong programming skills, experience developing and implementing new models in deep learning platforms such as PyTorch and TensorFlow, and broad knowledge of machine learning and deep learning methods are expected.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Tim Marks
    • Apply Now
  • CV1423: Health Monitoring from Video

    • MERL is seeking a highly motivated intern to conduct original research in the area of monitoring vital signs, such as heart rate and heart rate variability, from video of a person. The successful candidate will collaborate with MERL researchers to derive and implement new models, collect data, conduct experiments, and prepare results for publication. The candidate should be a PhD student in computer vision with a strong publication record and experience in computer vision, signal processing, machine learning, and health monitoring. Strong programming skills (C/C++, Python, Matlab, etc.) are expected.

    • Research Areas: Artificial Intelligence, Computer Vision, Signal Processing
    • Host: Tim Marks
    • Apply Now
  • CD1418: Fundamental Methods for Stochastic Constrained Control

    • MERL is seeking a motivated and qualified individual to conduct research on model predictive and constrained control for stochastic systems. The ideal candidate should have a solid background in mathematics, model predictive control, constrained control, stochastic systems, and optimization. PhD students in mechanical or electrical engineering with focus on control are encouraged to apply. The intern will collaborate with MERL researchers in developing fundamental method for controlling stochastic systems with constraints. Start date for this internship is flexible and the expected duration is approximately 3-6 months.

    • Research Areas: Control
    • Host: Claus Danielson
    • Apply Now
  • CD1401: Formal Synthesis for Planning and Control for Autonomous Systems

    • The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to conduct research on planning and control by formal methods, in particular temporal logics specifications and their synthesis by mixed-integer inequalities. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Computer Science or related program, with focus on Control Theory. The ideal candidate will have experience in (one or more of) formal methods, particularly temporal logics and signal temporal logics, reachability analysis, abstractions of dynamical systems, hybrid predictive control, and mixed integer programming. Good programming skills in Matlab (or alternatively Python) are required, working knowledge of C/C++ is a plus. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.

    • Research Areas: Control, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CD1400: Autonomous Vehicle Planning and Control

    • The Control and Dynamical Systems (CD) group at MERL is seeking highly motivated interns at different levels of expertise to conduct 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. PhD students will be considered for algorithm development and analysis, and property proving. Master students will be considered for development and implementation in a scaled robotic test bench for autonomous vehicles. For algorithm development and analysis it is highly desirable to have deep background in one or more among: sampling-based planning methods, particle filtering, model predictive control, reachability methods, formal methods and abstractions of dynamical systems, and experience with their implementation in Matlab/Python/C++. For algorithm implementation, it is required to have working knowledge of Matlab, C++, and ROS, and it is a plus to have background in some of the above mentioned methods. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.

    • Research Areas: Artificial Intelligence, Control, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CD1377: 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
  • CD1300: 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: Bram Goldsmith
    • Apply Now
  • CD1391: 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 candidates 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, Signal Processing
    • Host: Karl Berntorp
    • Apply Now
  • CD1399: Optimization Algorithms for Stochastic Predictive Control

    • MERL is looking for a highly motivated individual to work on tailored numerical optimization algorithms and applications of stochastic learning-based model predictive control (MPC) methods. The research will involve the study and development of novel optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: stochastic MPC (e.g., scenario trees or tube MPC), convex and non-convex optimization, machine learning, numerical optimization and (inverse) optimal control. PhD students in engineering or mathematics with a focus on stochastic (learning-based) MPC 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 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
    • Host: Rien Quirynen
    • Apply Now
  • CD1405: Mechanism design for mobility

    • Mobility; externalities; mechanism design. We are looking for a talented and driven individual to help us design an efficient and equitable mobility solution. This position requires a deep understanding of mechanism design and at least some programming ability. Preference will be given to candidates with a background in transportation.

    • Research Areas: Optimization
    • Host: Uroš Kalabić
    • 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