Internship Openings

7 / 27 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).


  • CA1399: Optimization Algorithms for Stochastic Predictive Control

    • MERL is looking for a highly motivated individual to work on tailored numerical optimization algorithms and applications of stochastic learning-based model predictive control (MPC) methods. The research will involve the study and development of novel optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: stochastic MPC (e.g., scenario trees or tube MPC), convex and non-convex optimization, machine learning, numerical optimization and (inverse) optimal control. PhD students in engineering or mathematics with a focus on stochastic (learning-based) MPC or numerical optimization are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab 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.

    • Research Areas: Control, Machine Learning, Optimization
    • Host: Rien Quirynen
    • 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: Bram Goldsmith
    • 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
  • MD1370: Machine Learning based DPD for Power Amplifier

    • MERL is looking for a talented intern to work on the next generation Digital-predistortion algorithms for power amplifier linearization such as 5G. The development of a DPD system involves aspects of signal processing and statistical algorithm design, RF components and instrumentation, digital hardware and software. It is therefore both a challenging and intellectually rewarding experience. This will involve MATLAB coding, interfacing to test equipment such as power sources, signal generators and analyzers and construction and calibration of RF component assemblies. The ideal candidate should have knowledge and experience in adaptive signal processing, machine learning, and radio communication. Good practical laboratory skills are needed. RF semiconductor devices and circuit knowledge is a plus. Duration is 3 to 6 months.

    • Research Areas: Communications, Electronic and Photonic Devices, Machine Learning, Signal Processing
    • Host: Rui Ma
    • Apply Now
  • SP1448: Intelligent Coding

    • The Signal Processing group at MERL is seeking a highly motivated, qualified individual to join our 3-month 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, coded modulation design, signal processing, deep learning, quantum computing, and molecular computing.

    • Research Areas: Communications, Machine Learning, Signal Processing
    • Host: Toshi Koike-Akino
    • 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