Internship Openings

14 / 46 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).


  • SA1320: Deep Network Information Security

    • We are seeking graduate students interested in helping advance the field of network and information security using the latest developments in deep learning. 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 deep networks, natural language processing, and network security. The duration of the internship is expected to be 3-6 months. The internship can be scheduled either during the summer or at another time of year.

    • Research Areas: Machine Learning, Speech & Audio
    • Host: Bret Harsham
    • Apply Now
  • CD1298: Theoretical and computational aspects of mean-field control

    • We are looking for a graduate student intern to work on theoretical and computational aspects of mean-field control and mean-field games. An ideal candidate will be a graduate student working on MFC/MFG or Optimal Transport. Expertise in TWO or more of the following areas is required: 1). Optimal control 2). Control of PDEs 3). Geometric methods of dynamical systems theory 4). Statistical Mechanics 5). Stochastic analysis 6). Optimal Transport. Ph.D. students from top programs in engineering, physics, applied math are encouraged to apply. The duration of the internship will be 3-6 months. Publication of results is highly encouraged.

    • Research Areas: Applied Physics, Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Piyush Grover
    • Apply Now
  • CD1323: Bayesian Inference and Learning

    • The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to advance the frontiers in the intersection of bayesian inference and learning. The ideal candidate has previous experience with at least some of sequential Monte Carlo methods, Gaussian processes, Kalman filtering, and target tracking. The candidate is expected to possess strong abilities in algorithm development and analysis. The internship start date is flexible. The duration is approximately 3 months, with possible extension. Publication of the results produced during the internship is expected.

    • Research Areas: Dynamical Systems, Machine Learning, Signal Processing
    • Host: Jay Thornton
    • 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
  • CD1295: Modeling and data-assimilation of atmospheric flows

  • CD1296: Optimization and Control of Thermo-fluid Systems

  • CV1273: Exploring boosted deep networks for computer vision

    • MERL is seeking an intern with expertise in neural networks and machine learning in general to work on a project exploring the combination of boosting with deep networks for applications to computer vision problems. The ideal candidate would be proficient in TensorFlow or PyTorch and have a strong machine learning and computer vision background.

    • Research Areas: Computer Vision, Machine Learning
    • Host: Mike Jones
    • Apply Now
  • CV1269: 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. Proficiency in Python programming is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Jeroen van Baar
    • Apply Now
  • CV1304: Uncertainty Estimation for Deep Network Predictions

    • While deep networks have been highly successful at visual regression problems such as face landmark estimation and skeleton tracking, relatively little work has been done on estimating their prediction error. 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 ideal candidate would be a senior PhD student in computer vision and machine learning with a strong publication record and experience in deep learning-based facial 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
  • CV1327: Graph neural networks for 3D point cloud processing

    • MERL is looking for a self-motivated intern with expertise in deep neural networks to work on 3D point cloud processing. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision. Proficiency in deep learning toolbox such as PyTorch and Tensorflow is necessary. Knowledge on signal processing and graph mining is a plus but is not strictly required. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.

    • Research Areas: Computer Vision, Machine Learning, Signal Processing
    • Host: Siheng Chen
    • Apply Now
  • CV1283: Understanding of Deep Learning

    • MERL is looking for a self-motivated intern to work on understanding of deep learning. The ideal candidate would be a Ph.D. student with a strong background in machine learning, optimization and computer vision. Proficiency in deep learning toolbox such as PyTorch and Tensorflow is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.

    • Research Areas: Computer Vision, Machine Learning, Optimization
    • Host: Ziming Zhang
    • Apply Now
  • CV1303: 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 ideal candidate would be a senior PhD student in computer vision with experience in GANs and related deep learning methods, as well as strong general knowledge in machine learning. Strong programming skills in Python, flexibility working across various deep learning platforms (e.g., TensorFlow and PyTorch), and previous experience coding GANs are expected.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Tim Marks
    • Apply Now
  • SP1278: Low Latency Wireless Networking

    • MERL is seeking a highly motivated, 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 low latency networking. The candidate is expected to develop innovative low latency technology in wireless networks. The candidates should have knowledge of low latency networking such as time sensitive networking. Knowledge of wireless network standards such as IEEE 802.11 and simulation tools such as OMNeT++ is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications, Machine Learning, Multi-Physical Modeling
    • Host: Jianlin Guo
    • Apply Now
  • SP1254: 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, 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: Computational Sensing, Machine Learning, Signal Processing
    • Host: Hassan Mansour
    • Apply Now