Internship Openings

6 / 56 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).


  • 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
  • CD1296: Optimization and Control of Thermo-fluid Systems

  • CD1295: Modeling and data-assimilation of atmospheric flows

  • DA1289: Robot Learning

    • MERL is looking for a highly motivated intern to work on developing algorithms for robot learning. Successful candidate will collaborate with MERL researchers to design, analyze, and implement new algorithms, conduct experiments, and prepare results for publication. The candidate should have a strong background in reinforcement learning, Imitation Learning (or Learning from Demonstrations, LfD), machine learning and robotics. Prior experience of working with robotic systems is required. The candidate should be comfortable implementing the developed algorithms in Python and should have prior experience working with ROS. Prior exposure to deep learning and hands-on experience with packages such as Keras, TensorFlow, or Theano is a plus. The candidate is expected to be a PhD student in Computer Science, Electrical Engineering, Operations Research, Statistics, Applied Mathematics, or a related field, with relevant publication record. Expected duration of the internship is at least 3 months. Interested candidates are encouraged to apply with their recent CV with list of related publications and links to GitHub repositories (if any).

    • Research Areas: Artificial Intelligence, Data Analytics, Machine Learning, Robotics
    • Host: Devesh Jha
    • Apply Now
  • DA1293: Machine Learning

    • The Data Analytics Group at MERL is seeking a highly motivated, qualified individual to join our internship program in the summer of 2019. There are multiple opportunities for contributions. Ideal candidates would have a strong background in Generative Models with a particular interest in tractable probabilistic models for continuous variables, approximate DNN-based generative methods (ex. VAE, Boltzman Machines), Reinforcement Learning with experience in an applied setting, classical and deep machine learning methods, or time series analytics. All candidates are expected to have strong programming skills in Python and a good understanding of object-oriented programming and algorithms. Candidates who hold a PhD or in their senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Data Analytics
    • Host: Emil Laftchiev
    • Apply Now
  • DA1297: Visuomotor Modelling for Learning Robots

    • The Data Analytics group at MERL is seeking a highly motivated intern to work on the development of dynamical models for robot learning. The target applications will be the model and control of real robotic systems with end-to-end type algorithms. The successful candidate will collaborate with MERL researchers to develop and implement new modelling techniques in high dimensional space (e.g. image space) to be used in a Reinforcement Learning framework, conduct experiments on the robots and achieve scientific contributions for publications and/or patents. Ideal candidate would be senior PhD student with experience in one or more of the following areas: machine learning modelling techniques (such as Gaussian Processes, Deep Learning) and Reinforcement Learning. Strong programming skills in Python and familiarity with ROS are expected. Previous experience in working with robotics platforms and in computer vision projects is a plus.

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