Internship Openings

16 / 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, Sudan and Syria (Country Groups E:1 and E:2 of Part 740, Supplement 1, of the U.S. Export Administration Regulations).


  • CV1372: 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/webly-supervised learning, 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 expected 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
  • CV1386: Machine Learning for Computer Vision

    • MERL is looking for a self-motivated intern to work on machine learning for computer vision. There are several available topics to choose from. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision. Publication in a top-tier machine learning or computer vision venue (NIPS, CVPR, ECCV, ICCV, ICML, PAMI, etc) is preferred. Proficiency in Python programming is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The internship would be 3-6 months, and the start date is flexible.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Jeroen van Baar
    • 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
  • 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
  • DA1367: Safe reinforcement and deep learning algorithms

    • MERL is seeking a motivated and qualified individual to conduct research in safe reinforcement learning (RL) and deep learning algorithms for robotics applications. The ideal candidate should have solid background in RL. Knowledge of deep learning algorithms is a plus. Publication of the results produced during the internship is anticipated. Duration of the internship is expected to be 3 months. Start date is flexible.

    • Research Areas: Artificial Intelligence, Control, Robotics
    • Host: Mouhacine Benosman
    • Apply Now
  • DA1396: Machine learning for Contact-rich Robotic Manipulation

    • MERL is looking for a highly motivated individual to work on contact-rich robotic manipulation applications. The ideal candidate is expected to have expertise both in machine learning techniques, such as Gaussian Process Regression and Deep Neural Networks, as well as in reinforcement learning algorithms. In addition, having experience with robotic systems would be considered a significant plus. The candidate will be expected to develop novel algorithms and possibly implement them on robotic systems. Proficiency in Python programming is necessary, and experience with ROS would be a plus. The candidate will collaborate closely with MERL researchers. Start date for this internship is flexible, and the duration is expected to be 3-6 months.

    • Research Areas: Artificial Intelligence, Data Analytics, Machine Learning, Robotics
    • Host: Diego Romeres
    • Apply Now
  • SP1430: WiFi Sensing

    • The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in wireless sensing using communication signals such as 5G, WiFi, and Bluetooth. Previous experience on occupancy sensing, people counting, localization, device-free pose/gesture recognition with machine learning approaches is highly preferred. Familiarity with IEEE 802.11 (ac/ad/ay)standards is a plus. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, collect real-world channel measurements, and prepare results for publication. Senior Ph.D. students with research focuses on wireless communications, machine learning, signal processing, 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
  • SP1371: Object Tracking and Perception for Autonomous Driving

    • The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in automotive radar-based object tracking and perception for autonomous driving. Previous experience on multiple (point and extended) object tracking, data association, and data-driven object detection/tracking is highly preferred. Knowledge about automotive radar schemes (MIMO array and waveform modulation (FMCW, PMCW, and OFDM)) and hands-on experience on open automotive datasets are a plus. Knowledge on vehicle dynamics is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, conduct field measurements, data analysis (Python & MATLAB), 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, Computational Sensing, Dynamical Systems, Machine Learning, Signal Processing
    • Host: Perry Wang
    • Apply Now
  • SP1366: 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
  • SP1368: AI-enhanced security

    • MERL is seeking a highly motivated and qualified intern to work on AI-enhanced security. The candidate is expected to develop innovative AI technologies for cybersecurity applications. Candidates should have strong knowledge and hands on experience in the areas of neural network and learning techniques, such as feature extraction, machine learning, deep learning, shallow learning, and distributed learning. Proficient programming skills with Python, Matlab, and C++, and strong mathematical analysis will be required to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Artificial Intelligence, Machine Learning, Signal Processing
    • Host: K.J. Kim
    • 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
  • SP1428: Big data learning

    • MERL is seeking a highly motivated, qualified individual to join the Signal Processing group for a three-month internship program in summer 2020. The candidate will be expected to carry out research on data analysis and applications of Deep/Machine Learning to topics in communication systems and networking. Anomaly detection methods of are particular interest, but specific work plan can be flexible. The ideal candidate should have knowledge of machine learning such as neural networks and data analysis as well as familiarity with wireless communication and networking technologies. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Artificial Intelligence, Data Analytics, Machine Learning
    • Host: Jianlin Guo
    • Apply Now
  • SP1404: Photonic device design using deep learning

    • MERL is seeking a highly motivated, qualified individual to join our internship program and conduct research in the area of photonic and nanophotonic device design and optimization using deep learning. The ideal candidate should have a strong background in the simulation (such as Lumerical FDTD and MODE), design, and testing of active and passive devices for optical communications, as well as hands-on experience in deep learning (such as autoencoders and GAN using Python and Tensorflow/keras/pytorch). Experience in silicon photonics, photonic crystal, plasmonicss, optimization algorithms, machine learning, photonic device fabrication/measurements, and mask designs for InP and silicon photonic MPW would be considered an asset. Candidates who hold a Ph.D. or in their senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Artificial Intelligence, Electronic and Photonic Devices
    • Host: Keisuke Kojima
    • 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
  • 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