Internship Openings

14 / 58 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.


  • 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
  • SP1718: Brain-Machine Interface

  • SP1752: Machine Learning for Electric Design Automation

    • MERL is seeking a highly motivated and qualified intern to join the Signal Processing group for an internship program. The ideal candidate will be expected to carry out research on machine learning for automated design synthesis to improve hardware efficiency of various digital signal processing algorithms. The candidate is expected to have solid knowledge of deep learning, reinforcement learning, symbolic learning, decision making, and graph neural networks. Hands-on experience of high-level synthesis, FPGA prototyping, verilog, and general digital signal processing is a plus.

    • Research Areas: Artificial Intelligence, Electric Systems, Machine Learning, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • SP1734: Robust Machine Learning

    • MERL is seeking a highly motivated and qualified intern to work on robust machine learning techniques. The intern will collaborate with MERL researchers on developing novel approaches to address the problem of adversarial examples. The ideal candidate would have research experience in robust machine learning methods and defenses against adversarial examples. A mature understanding of modern machine learning methods, proficiency with Python, and familiarity with deep learning frameworks are expected. Proficiency with other programming languages and software development experience is a plus. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply.

    • Research Areas: Artificial Intelligence, Machine Learning, Signal Processing
    • Host: Ye Wang
    • Apply Now
  • SP1748: Learning-based Wireless Sensing

    • The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in learning-based wireless sensing using communication signals (e.g., Wi-Fi) and other RF signals (such as millimeter-wave sensing waveforms). Expertise in deep learning in one of the following areas: localization, occupancy sensing, device-free pose/gesture recognition, skeleton tracking, and multi-modal fusion, is highly preferred. Familiarity with IEEE 802.11 (g/n/ac/ad/ay)standards 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 using MERL in-house testbed, and prepare results for 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, Communications, Computational Sensing, Machine Learning, 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.

    • Research Areas: Artificial Intelligence, Machine Learning, Signal Processing
    • Host: Toshi Koike-Akino
    • 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
  • CV1725: Few-Shot Action Recognition

    • MERL is looking for a self-motivated intern to work on problems at the intersection of video understanding and graph representation learning for solving few-shot action recognition problems. The ideal candidate would be a PhD student with a strong mathematical background in machine learning and computer vision and who has published at least one paper in a top-tier machine learning or computer vision venue (NIPS/CVPR/ECCV/ICCV/ICML/PAMI etc.). The candidate must have prior experience in using deep learning methods for video understanding (such as using Transformers) and self/unsupervised methods (such as contrastive learning). Proficiency in PyTorch is expected and familiarity with neural language models will be a plus. The intern will conduct original research with MERL researchers towards scientific publications. This internship requires work that can only be done at MERL.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Anoop Cherian
    • 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
  • CV1774: Modeling uncertainty in computer vision

    • We seek a highly motivated intern to conduct original research in the estimation and modeling of uncertainty in deep-learning-based computer vision tasks. 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 (or postdoc) in computer vision and machine learning with a strong publication record. 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. Previous experience in uncertainty estimation and modeling is preferred.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Tim Marks
    • Apply Now
  • CV1701: Computer Vision for Biased or Scarce Data

    • MERL is looking for a self-motivated intern to work on data scarcity and bias issues for computer vision. The topics in the scope include (but not limited to): domain adaptation, generative modeling, transfer/low-shot/unsupervised learning, low-shot image/video anomaly localization, multi-model or multi-modal fusion or distillation under limited data, etc. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Kuan-Chuan Peng
    • Apply Now
  • CV1773: Learning Neural Radiance Fields for Realistic Image Generation

    • MERL is seeking a highly motivated intern to conduct original research in data-driven realistic image generation that learns and combines implicit and explicit deep 3D models from unlabeled images. 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 (or postdoc) in computer vision with experience in implicit neural network models, GANs, and related deep learning methods, as well as good general knowledge in machine learning and a strong publication record. Previous experience with 3D face models and video generation is preferred. Strong programming skills in Python and flexibility working across various deep learning platforms (e.g., PyTorch and TensorFlow) are expected.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Tim Marks
    • 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
  • DA1768: Contact Modeling and Optimization

    • MERL is looking for a self-motivated and qualified candidate to work on modeling for contact phenomenon. Robotic manipulation is heavily affected by external contacts that can be modeled with physical and data driven models. We are interested in researching those models for analysis and control purposes. The ideal candidate is a PhD student and should have experience and records in multiple of the following areas. Contact modeling and robotic manipulation. Physic Engines like Mujoco, Bullet, Drake and sim2real gap problems. Machine learning techniques for modeling and control such as Gaussian Processes and Neural Networks. Experience in working with robotic systems. Knowledge in learning from demonstration algorithms and standard Reinforcement Learning algorithms is a plus. Proficiency in Python is required. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms that will lead to a scientific publication. Typical internship length is 3-4 months.

    • Research Areas: Artificial Intelligence, Dynamical Systems, Machine Learning, Robotics
    • Host: Diego Romeres
    • Apply Now