Internship Openings

23 / 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).


  • CD1383: Collaborative Estimation for Robotic Manipulators

    • MERL is seeking a highly skilled and self-motivated intern to conduct research on condition monitoring for robotic manipulators. The ideal candidate should have solid backgrounds in robotic manipulators, stochastic estimation methods for dynamical systems, and collaborative strategies over multi-agents. Experience of applying machine learning to dynamical systems is a strong plus. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, robotics, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 3 months.

    • Research Areas: Dynamical Systems, Machine Learning, Robotics
    • Host: Yebin Wang
    • Apply Now
  • CD1392: Statistical Estimation, Learning, and Control of Dynamical Systems

    • The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to conduct fundamental research on statistical estimation and control. The scope of the internship includes development of algorithms and property proving for estimation and control of stochastic dynamical systems. PhD students with expertise in several of sequential Monte Carlo methods, Gaussian processes, Gaussian-process state-space models, model predictive control, are welcome to apply. The candidate is expected to be proficient in Matlab, and publication of the results produced during the internship is expected. The internship duration is 3 months with flexible start date.

    • Research Areas: Control, Machine Learning, Optimization
    • Host: Karl Berntorp
    • Apply Now
  • CD1377: 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
  • CD1399: 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
  • 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
  • CD1393: Estimation and Learning of Decentralized Systems

    • The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to conduct research on estimation and learning of decentralized systems. The ideal candidate is a PhD candidate in electrical engineering, statistics, or related fields, and has expertise in some of Kalman filters, particle filters, multiple model filters, Bayesian learning, centralized/decentralized sensor fusion, and optimization-based estimation. Previous experience in any of ground vehicle estimation and satellite navigation systems is a merit. Publication of relevant results in conference proceedings and journals is expected, and so is capability of implementing algorithms in Matlab. The expected duration of the internship is 3-6 months with flexible start date.

    • Research Areas: Control, Machine Learning, Signal Processing
    • Host: Karl Berntorp
    • 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
  • 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
  • CV1374: Efficient Deep Networks

    • MERL is looking for a self-motivated intern to work on the problem of learning deep networks that are both memory and time efficient. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision and some experience with techniques related to model compression, knowledge distillation, etc. Proficiency in Python programming and TensorFlow or PyTorch 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
    • Host: Mike Jones
    • 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
  • CV1365: Machine learning for 3D computer vision

    • MERL is looking for a self-motivated intern to work on machine learning for 3D 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: Computer Vision, Machine Learning
    • Host: Siheng Chen
    • 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
  • 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
  • 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
  • CV1375: Computer Vision for Robotic Manipulation

    • MERL is looking for a highly motivated intern to work on computer vision for robotic manipulation. There are several available topics to choose from including active perception, grasp detection, and intent recognition for human-robot interaction. The ideal candidate would be a Ph.D. student with a strong background in computer vision, deep learning, and/or robotics. Proficiency in Python programming is necessary and experience with ROS is a plus. Successful candidate will collaborate with MERL researchers to develop algorithms, conduct experiments, and prepare manuscripts for scientific publications. Start date is flexible and expected duration of the internship is at least 3 months. Interested candidates are encouraged to apply with their recent CV, list of related publications, and/or links to GitHub repositories (if any).

    • Research Areas: Computer Vision, Machine Learning, Robotics
    • Host: Siddarth Jain
    • 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
  • SP1414: Learning for inverse problems and dynamical systems

    • The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that solve inverse problems in computational sensing that incorporate deep learning architectures for a variety of sensing applications. The project goal is to improve the performance and develop an analysis of algorithms used for inverse problems by incorporating new tools from machine learning and artificial intelligence. 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, plug-and-play priors, learning-based modeling for imaging, learning theory for computational imaging, and Koopman theory/dynamic mode decomposition. 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, Dynamical Systems, Machine Learning, Signal Processing
    • Host: Hassan Mansour
    • 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
  • 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
  • SP1369: Advanced Vehicular Communications

    • 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 the vehicular communications and networking. The candidate is expected to develop innovative technology to achieve reliable and low latency V2X communications in the vehicular networks. The candidates should have knowledge of the vehicular communication technology such as IEEE 802.11p or 3GPP C-V2X. Knowledge of the vehicular control is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications, Control, Machine Learning
    • Host: Jianlin Guo
    • 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
  • SP1370: 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