Internship Openings

17 / 29 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).

Rising to the challenges of COVID-19

The COVID pandemic has impacted every aspect of life-how we live, work, and interact. At MERL, we are committed to maintaining our internship program through these challenging times.

MERL continues to actively seek candidates for research internships -- some of the posted positions are immediately available, while others target the summer of 2021. Please consider applying for positions of interest. Our researchers will follow up to schedule an interview by phone or video conference for qualified candidates.

Due to the situation with the COVID-19 pandemic, our current internships are mostly remote. Next summer we hope the situation will be better and our internships will be at MERL, but if it is not, most internships will continue to be remote. However, some of the internships require onsite work. Please check for any specific requirements for onsite work in the job description.


  • CA1565: Connected Vehicle Driver Assistance Systems

    • MERL is seeking a highly motivated qualified intern to collaborate with the Control for Autonomy team and the Signal Processing group in the development of Advanced Driver Assistance Systems (ADAS) for Connected Vehicles. The intern will collaborate in the development of methods for distributed learning and optimization of ADAS using data-sharing between connected vehicles and infrastructure. The ideal candidate has knowledge of machine learning, optimization and connected vehicles. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) is a plus. Good programming skills in MATLAB, Python, or C/C++ are required. 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, starting in Spring or Summer 2021, but later starting periods may also be considered. 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
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1529: Energy Management for Electric Vehicles

    • MERL is looking for a highly motivated intern to conduct research on data-driven energy management strategies for (hybrid) electric vehicles. The candidate will develop methods that use data, e.g., of human drivers or traffic conditions, in order to improve the control of electric vehicles. The ideal candidate will have experience in either one or multiple of the following topics: model predictive control, machine learning, statistical learning, numerical optimization, and (inverse) optimal control. Prior experience with (hybrid) electric vehicles is a plus. Good programming skills in MATLAB, Python, or C/C++ are required. PhD students in engineering or mathematics with a focus on 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 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
  • CA1519: Estimation for High-Precision Positioning

    • MERL is seeking a highly motivated candidate for development of next-generation high-precision positioning methods for autonomous systems applications, e.g., autonomous driving. The candidate will work with the Control for Autonomy team and the Signal Processing group in developing satellite-based positioning methods using information from multiple sources. Previous experience with at least some of the Bayesian inference, distributed estimation, satellite navigation systems, is highly desirable. Solid knowledge in MATLAB is required, working experience in C/C++ is desired, and previous experience with satellite navigation packages such as RTKLib is a merit. PhD candidates meeting the above requirements are encouraged to apply. The expected duration of the internship is 3-6 months with flexible start date. 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
  • CA1531: Learning-based multi-agent motion planning

    • MERL is seeking a highly motivated intern to research multi-agent motion planning by combining optimization-based methods with machine learning. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in multi-agent motion planning, machine learning (especially supervised, reinforcement, and safe ML), and convex and non-convex optimization. A successful internship will result in innovative methods for multiagent planning, in the development of well-documented (Python/MATLAB) code for validating the proposed methods, and in the submission of relevant results for publication in peer-reviewed conference proceedings and journals. The expected duration of the internship is 3 months with a flexible start date in the Spring/Summer 2021. 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, Optimization, Robotics
    • Host: Abraham P. Vinod
    • Apply Now
  • CA1528: Learning-Based Stochastic Predictive Control

    • MERL is looking for highly motivated interns to work in the domain of data-based controller design and algorithms for stochastic model predictive control (MPC) methods. The research involves the derivation, implementation, and validation of novel algorithms for optimization-based/data-driven control for industrial applications, e.g., related to autonomous driving and robotics. The ideal candidate has experience in either one or multiple of the following topics: stochastic MPC (e.g., scenario trees or tube MPC), (inverse) optimal control, convex and nonconvex optimization, parallel processing, real-time optimization, machine learning, statistical learning, and Bayesian inference. PhD students in engineering or mathematics, especially with a focus on stochastic and learning-based control or numerical optimization, 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. 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
    • Host: Karl Berntorp
    • Apply Now
  • MD1377: 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
  • 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
  • CV1568: Uncertainty Estimation in 3D Face Landmark Tracking

    • We are seeking a highly motivated intern to conduct original research extending MERL's work on uncertainty estimation in face landmark localization (the LUVLi model) to the domains of 3D faces and video sequences. 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. Experience in deep learning-based face landmark estimation, video tracking, and 3D face modeling is preferred. Strong programming skills, experience developing and implementing new models in deep learning platforms such as PyTorch, 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
  • CV1546: Vibration analysis in video sequences

    • MERL is looking for a self-motivated intern to work on vibration analysis in video sequences. The ideal candidate would be a Ph.D. student with a strong background in machine learning, optimization and computer vision. Experience in computational photography and MATLAB/Python is a plus. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The internship is for a minimum of 3 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: Computer Vision, Machine Learning, Optimization
    • Host: Jeroen van Baar
    • Apply Now
  • CV1569: Robot learning from videos of human demonstrations

    • MERL is looking for a highly motivated and qualified intern to work on developing algorithms for robot learning from videos of human demonstrations. The ideal candidate would be a current Ph.D. student with a strong background in computer vision, deep learning, and robotics. Familiarity with imitation learning, learning from demonstrations (LfD), reinforcement learning, and machine learning for robotics will be valued. Proficiency in Python programming is necessary and experience in working with a physics engine simulator like Mujoco or pyBullet is a plus. A successful candidate will collaborate with MERL researchers and publication of the relevant results is expected. Start date is flexible and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics. 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, Computer Vision, Machine Learning, Robotics
    • Host: Jeroen van Baar
    • Apply Now
  • SA1611: Audio source separation and sound event detection

    • We are seeking a graduate student interested in helping advance the fields of source separation, speech enhancement, and sound event detection/localization in challenging multi-source and far-field scenarios. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. The ideal candidate would be a senior Ph.D. student with experience in audio signal processing, microphone array processing, probabilistic modeling, and deep learning techniques requiring minimal supervision (e.g., unsupervised, weakly-supervised, self-supervised, or few shot learning). The internship will take place during fall/winter 2021 with an expected duration of 3-6 months and a flexible start date. 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: Machine Learning, Speech & Audio
    • Host: Gordon Wichern
    • Apply Now
  • SP1517: AI-based spectrum management for 5G wireless networks and beyond

    • MERL is seeking a highly motivated, qualified intern to join a thirteen weeks internship program. The ideal candidate will be expected to carry out research on emerging 5G wireless networks and beyond for industrial applications. The candidate is expected to develop innovative spectrum-based traffic recognition and optimal scheduling for local spectrum access. Candidates should have strong knowledge about 5G networks, spectrum management, cognitive radio, and neural network. Proficient programming skills with MATLAB, C++, Python (Pytorch), experience with ns-3 simulator, 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. 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, Communications, Machine Learning, Signal Processing
    • Host: K.J. Kim
    • Apply Now
  • SP1582: Source & Channel Coding

    • MERL is seeking a highly motivated, qualified individual to join our internship program of research on applied coding for data science. The ideal candidate is expected to possess an excellent background in channel coding, source coding, information theory, coding theory, coded modulation design, signal processing, deep learning, quantum computing, and molecular computing. 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: Communications, Machine Learning, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • SP1512: Mutual Interference Mitigation

    • The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in mutual interference mitigation for automotive radar. Previous experience in waveform design, radar detection under interference, joint communication and sensing, interference mitigation, and deep learning for radar is highly preferred. Knowledge about automotive radar schemes (MIMO and waveform modulation, e.g., FMCW, PMCW, and OFDM) is a plus. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments using MERL in-house testbed, and prepare results for patents and publication. Senior Ph.D. students with research focuses on signal processing, machine learning, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date.

    • Research Areas: Artificial Intelligence, Communications, Computational Sensing, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Signal Processing
    • Host: Perry Wang
    • Apply Now
  • SP1468: Quantum Machine Learning

    • MERL is seeking an intern to work on research for quantum machine learning (QML). The ideal candidate is an experienced PhD student or post-graduate researcher having an excellent background in quantum computing, deep learning, and signal processing. Proficient programming skills with PyTorch, Qiskit, and PennyLane will be additional assets to this position. Also note that we wish to fill this position as soon as possible and expect that the candidate will be available during this fall/winter. 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, Machine Learning, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • SP1537: Machine Learning for Wireless Communications

    • MERL is seeking an intern to work on machine learning for wireless communication systems. The ideal candidate would be an experienced PhD student or post-graduate researcher working in wireless communications with a focus on machine learning methods. The candidate should have a detailed knowledge of wireless communications, with some experience in machine learning, MIMO, and/or channel equalization preferred. Strong programming skills in Python and machine learning frameworks are essential. The expected duration of the internship is 3-6 months with flexible start date and length. 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, Communications, Machine Learning
    • Host: Ye Wang
    • Apply Now
  • DA1602: Reinforcement Learning for HVAC systems

    • MERL is looking for a self-motivated and qualified candidate to work on air flow control of Heating, Ventilation and Air Conditioning (HVAC) systems. The ideal candidate is a PhD student and should have experience and records in multiple of the following areas: fluid dynamics, control theory, reinforcement learning, familiarity with partial differential equations. Proficiency in Python and Matlab is required. The successful candidate will be expected to develop, in collaboration with MERL employees, a state of the art algorithms for air flow control that will lead to a scientific publication. Typical internship length 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: Applied Physics, Control, Machine Learning
    • Host: Diego Romeres
    • Apply Now