Internship Openings

17 / 28 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.

Working at MERL requires full authorization to work in the U.S and access to technology, software and other information that is subject to governmental access control restrictions, due to export controls. Employment is conditioned on continued full authorization to work in the U.S and the availability of government authorization for the release of these items, which might include without limitation, obtaining an export license or other documentation. MERL may delay commencement of employment, rescind an offer of employment, terminate employment, and/or modify job responsibilities, compensation, benefits, and/or access to MERL facilities and information systems, as MERL deems appropriate, to ensure practical compliance with applicable employment law and government access control restrictions.


  • CV0056: Internship - "Small" Large Generative Models for Vision and Language

    • MERL is looking for research interns to conduct research into novel architectures for "small" large generative models. We are currently exploring 0.5 - 2 billion parameter language models, text-to-image models and text-to-video models. Interesting research directions include (a) efficient learning for such models that improves the pareto front of current scaling laws for these sizes, (b) enhancing current transformer-based architectures, and (c) new architectural paradigms beyond transformers such as incorporating explicitly temporal designs. Prior experience with machine learning/computer vision/natural language processing research, and proficiency in building and experimenting with machine learning models using a framework like PyTorch are required. Candidates well into their PhD program with publications in top-tier machine learning, natural language processing or computer vision venues, ideally connected to building generative models, are strongly preferred. Candidates are also expected to collaborate with MERL researchers for preparing manuscripts for scientific publications based on the results obtained during the internship. Duration of the internship is 3 months with a flexible start date.

      Required Specific Experience

      • Research experience with recent vision and text generative models
      • Deep understanding of neural network architectures
      • Proficiency in machine learning frameworks like PyTorch

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Suhas Lohit
    • Apply Now
  • CV0051: Internship - Visual-LiDAR fused object detection and recognition

    • MERL is looking for a self-motivated intern to work on visual-LiDAR fused object detection and recognition using computer vision. The relevant topics in the scope include (but not limited to): open-vocabulary visual-LiDAR object detection and recognition, domain adaptation or generalization in visual-LiDAR object detection, data-efficient methods for visual-LiDAR object detection, small object detection with visual-LiDAR input, etc. The candidates with experiences of object recognition in LiDAR are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.

      Required Specific Experience

      • Experience with Python, PyTorch, and datasets with both images and LiDAR (e.g. the nuScenes dataset).

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Kuan-Chuan Peng
    • Apply Now
  • CV0050: Internship - Anomaly Localization for Industrial Inspection

    • MERL is looking for a self-motivated intern to work on anomaly localization in industrial inspection setting using computer vision. The relevant topics in the scope include (but not limited to): cross-view image anomaly localization, how to train one model for multiple views and defect types, how to incorporate large foundation models in image anomaly localization, etc. The candidates with experiences of image anomaly localization in industrial inspection settings (e.g., MVTec-AD or VisA datasets) and usage of large foundation models are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.

      Required Specific Experience

      • Experience with Python, PyTorch, and large foundation models (e.g. CLIP, ALIGN, etc.).

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Kuan-Chuan Peng
    • Apply Now
  • CV0084: Internship - Vital signs from video using computer vision and AI

    • MERL is seeking a highly motivated intern to conduct original research in estimating vital signs such as heart rate, heart rate variability, and blood pressure from video of a person. The successful candidate will use the latest methods in deep learning, computer vision, and signal processing to derive and implement new models, collect data, conduct experiments, and prepare results for publication, all in collaboration with MERL researchers. The candidate should be a Ph.D. student in computer vision with a strong publication record and experience in computer vision, signal processing, machine learning, and health monitoring. The successful candidate is expected to have published at least one paper in a top-tier computer vision or machine learning venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI, and possess strong programming skills in Python and Pytorch. Start date is flexible; duration should be at least 3 months.

      Required Specific Experience

      • Ph.D. student in computer vision or related field.
      • Strong programming skills in Python and Pytorch.
      • Published at least one paper in a top-tier computer vision or machine learning venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal Processing, Computational Sensing
    • Host: Tim Marks
    • Apply Now
  • CI0066: Internship - IoT Network Anomaly Detection

    • MERL is seeking a highly motivated and qualified intern to conduct research on IoT network anomaly detection and analysis. The candidate is expected to develop innovative anomaly detection technologies that can proactively detect and analyze network failure in large-scale IOT networks. The candidate should have knowledge of LLM/ML and anomaly detection. Knowledge of network log analysis and network protocol a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. Start date for this internship is flexible and the duration is 3 months. The responsibilities of this intern position include (i) research on anomaly detection in large-scale IoT networks; (ii) develop proactive network anomaly detection and analysis technologies; (iii) simulate and analyze the performance of developed technology.

    • Research Areas: Communications, Artificial Intelligence, Data Analytics, Signal Processing
    • Host: Jianlin Guo
    • Apply Now
  • CI0054: Internship - Anomaly Detection for Operations Technology Security

    • MERL is seeking a highly motivated and qualified intern to work on anomaly detection for operational technology security. The ideal candidate would have significant research experience in anomaly detection, machine learning, and cybersecurity for operational technology. A mature understanding of modern machine learning methods, proficiency with Python and PyTorch, and a relevant research publication history are expected. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. The expected duration is for 3 months with flexible start dates (but ideally in December or early January).

      Required Specific Experience

      • Proficiency with PyTorch framework.
      • Research publications in machine learning and anomaly detection.

    • Research Areas: Artificial Intelligence, Machine Learning, Data Analytics
    • Host: Ye Wang
    • Apply Now
  • CI0086: Internship - Trustworthy AI

    • Internship Opportunity: Trustworthy AI

      MERL is seeking passionate and skilled research interns to join our team focused on developing trustworthy, private, safe, and robust machine learning technologies. This is an exciting opportunity to make an impact on the field of AI safety, with the aim of publishing at leading AI research venues.
      What We're Looking For:
      • Advanced research experience with generative models related to the topics of AI safety, privacy, robustness, and trustworthiness
      • Hands-on skills for large language models (LLM), vision language models (VLM), large multi-modal models (LMM), foundation models (FoMo)
      • Deep understanding of state-of-the-art machine learning methods
      • Proficiency in Python and PyTorch
      • Familiarity with other relevant deep learning frameworks
      • Ph.D. candidates who have completed at least half of their program
      Internship Details:
      • Duration: approximately 3 months
      • Flexible start dates available
      • Objective: publish research results at leading AI research venues
      If you're a highly motivated individual with a passion for tackling AI safety and privacy challenges, we want to hear from you! This internship offers a unique chance to work on meaningful AI research projects, combined with the opportunity to publish and add to your thesis.

    • Research Areas: Artificial Intelligence, Machine Learning
    • Host: Ye Wang
    • Apply Now
  • CI0080: Internship - Efficient AI

    • We are on the lookout for passionate and skilled interns to join our cutting-edge research team focused on developing efficient machine learning techniques for sustainability. This is an exciting opportunity to make a real impact in the field of AI and environmental conservation, with the aim of publishing at leading AI research venues. What We're Looking For:

      • Advanced research experience in generative models and computationally efficient models
      • Hands-on skills for large language models (LLM), vision language models (VLM), large multi-modal models (LMM), foundation models (FoMo)
      • Deep understanding of state-of-the-art machine learning methods
      • Proficiency in Python and PyTorch
      • Familiarity with various deep learning frameworks
      • Ph.D. candidates who have completed at least half of their program
      Internship Details:
      • Duration: approximately 3 months
      • Flexible start dates available
      • Objective: publish research results at leading AI research venues
      If you are a highly motivated individual with a passion for applying AI to sustainability challenges, we want to hear from you! This internship offers a unique chance to work on meaningful projects at the intersection of machine learning and environmental sustainability.

    • Research Areas: Artificial Intelligence, Machine Learning
    • Host: Toshi Koike-Akino
    • Apply Now
  • CI0083: Internship - Human-Machine Interface with Biosignal Processing

    • Internship Opportunity: Human-Machine Interface with Biosignal Processing MERL is excited to announce an internship opening for a talented researcher to join our team. We are looking for an individual to contribute to cutting-edge research in human-machine interfaces (HMI) using multi-modal bio-sensors. This is an exciting opportunity to make a real impact in the field of human-machine interaction and biosignal processing, with the aim of publishing at leading research venues. Ideal Candidate:

      • Experienced PhD student or post-graduate researcher
      • Strong background in brain-machine interface (BMI)
      • Proficient in deep learning and mixed reality (XR)
      • Skilled in robot manipulation, bionics, and bio sensing
      • Digital modeling of human and environment
      • Hands-on experience in Unity3d, ROS, OpenBCI, and XR headsets
      If you are passionate about advancing technology in these areas, we encourage you to apply and be part of our innovative research team!

    • Research Areas: Artificial Intelligence, Machine Learning, Robotics, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • CI0082: Internship - Quantum AI

    • Internship Opportunity: Quantum AI MERL is excited to announce an internship opportunity in the field of Quantum Machine Learning (QML) and Quantum AI (QAI). We are seeking a highly motivated and talented individual to join our research team. This is an exciting opportunity to make a real impact in the field of quantum computing and AI, with the aim of publishing at leading research venues. Responsibilities:

      • Conduct cutting-edge research in quantum machine learning.
      • Collaborate with a team of experts in quantum computing, deep learning, and signal processing.
      • Develop and implement algorithms using PyTorch and PennyLane.
      • Publish research results at leading research venues.
      Qualifications:
      • Currently pursuing a PhD or a post-graduate researcher in a relevant field.
      • Strong background and solid publication records in quantum computing, deep learning, and signal processing.
      • Proficient programming skills in PyTorch and PennyLane are highly desirable.
      What We Offer:
      • An opportunity to work on groundbreaking research in a leading research lab.
      • Collaboration with a team of experienced researchers.
      • A stimulating and supportive work environment.
      If you are passionate about quantum machine learning and meet the above qualifications, we encourage you to apply. Please submit your resume and a brief cover letter detailing your research experience and interests. Join us at MERL and contribute to the future of quantum machine learning!

    • Research Areas: Artificial Intelligence, Machine Learning, Signal Processing, Applied Physics
    • Host: Toshi Koike-Akino
    • Apply Now
  • CA0055: Internship - Human-Collaborative Loco-Manipulation Robots

    • MERL seeks graduate students passionate about robotics to contribute to the development of a framework for legged robots with manipulator arms to collaborate with human in executing various tasks. The work will involve multi-domain research including planning and control, manipulation, and possibly vision/perception. The methods will be implemented and evaluated in high performance simulators and (time-permitting) in actual robotic platforms. The results of the interns are expected to be published in top-tier robotic conferences and/or journal. The internship should start in January 2025 (exact date is flexible) with an expected duration 3-6 months depending on agreed scope and intermediate progress.

      Required Specific Experience

      • Current/Past enrollment in a PhD program in Mechanical, Aerospace, Electrical Engineering, with a concentration in Robotics
      • 2+ years of research in at least some of: machine learning, optimization, control, path planning, computer vision
      • Experience in design and simulation tools for robotics such as ROS, Mujoco, Gazebo, Isaac Lab
      • Strong programming skills in Python and/or C/C++
      Additional Desired Experience
      • Development of planning and control methods in robotic hardware platforms
      • Acquisition and processing of multimodal sensor data, including force/torque and proprioceptive sensors
      • Prior experience in human-robot interaction, legged locomotion, mobile manipulation

    • Research Areas: Robotics, Control, Machine Learning, Optimization, Computer Vision, Artificial Intelligence
    • Host: Stefano Di Cairano
    • Apply Now
  • EA0076: Internship - Machine Learning for Electric Motor Design

    • MERL is seeking a motivated and qualified intern to conduct research on machine learning based electric motor design and optimization. Ideal candidates should be Ph.D. students with a solid background and publication record in electric machine design, optimization, and machine learning. Hands-on experience with the implementation of optimization algorithms, machine learning and deep learning methods is required. Strong programming skills using Python/PyTorch are expected. Knowledge and experience with electric machine principle, design and finite-element analysis are highly desirable. Start date for this internship is flexible and the duration is about 3 months.

    • Research Areas: Artificial Intelligence, Machine Learning, Optimization
    • Host: Bingnan Wang
    • Apply Now
  • EA0070: Internship - Multi-modal sensor fusion

    • MERL is looking for a self-motivated intern to work on multi-modal sensor fusion for health condition monitoring and predictive maintenance of motor drive systems. The ideal candidate would be a Ph.D. candidate in electrical engineering or computer science with solid research background in signal processing and machine learning. Experience in motor drive system is a plus. The intern is expected to collaborate with MERL researchers to collect data, explore multi-modal data relationship, and prepare manuscripts for publications. The total duration is anticipated to be 3 months and the start date is flexible.

      Required Specific Experience

      • Experience with multi-modal sensor fusion.

    • Research Areas: Data Analytics, Electric Systems, Machine Learning, Signal Processing, Artificial Intelligence
    • Host: Dehong Liu
    • Apply Now
  • SA0044: Internship - Multimodal scene-understanding

    • We are looking for a graduate student interested in helping advance the field of multimodal scene understanding, focusing on scene understanding using natural language for robot dialog and/or indoor monitoring using a large language model. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern''''s doctoral work. The ideal candidates are senior Ph.D. students with experience in deep learning for audio-visual, signal, and natural language processing. Good programming skills in Python and knowledge of deep learning frameworks such as PyTorch are essential. Multiple positions are available with flexible start date (not just Spring/Summer but throughout 2024) and duration (typically 3-6 months).

      Required Specific Experience

      • Experience with ROS2, C/C++, Python, and deep learning frameworks such as PyTorch are essential.

    • Research Areas: Artificial Intelligence, Computer Vision, Control, Machine Learning, Robotics, Speech & Audio
    • Host: Chiori Hori
    • Apply Now
  • SA0045: Internship - Universal Audio Compression and Generation

    • We are seeking graduate students interested in helping advance the fields of universal audio compression and generation. We aim to build a single generative model that can perform multiple audio generation tasks conditioned on multimodal context. The interns will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern's doctoral work. The ideal candidates are Ph.D. students with experience in some of the following: deep generative modeling, large language models, neural audio codecs. The internship typically lasts 3-6 months.

    • Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    • Host: Sameer Khurana
    • Apply Now
  • SA0041: Internship - Audio separation, generation, and analysis

    • We are seeking graduate students interested in helping advance the fields of generative audio, source separation, speech enhancement, spatial audio, and robust ASR in challenging multi-source and far-field scenarios. The interns will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern's doctoral work. The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, spatial audio reproduction, probabilistic modeling, deep generative modeling, and physics informed machine learning techniques (e.g., neural fields, PINNs, sound field and reverberation modeling). Multiple positions are available with flexible start dates (not just Spring/Summer but throughout 2025) and duration (typically 3-6 months).

    • Research Areas: Speech & Audio, Machine Learning, Artificial Intelligence
    • Host: Jonathan Le Roux
    • Apply Now
  • SA0040: Internship - Sound event and anomaly detection

    • We are seeking graduate students interested in helping advance the fields of sound event detection/localization, anomaly detection, and physics informed deep learning for machine sounds. The interns will collaborate with MERL researchers to derive and implement novel algorithms, record data, conduct experiments, integrate audio signals with other sensors (electrical, vision, vibration, etc.), and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern's doctoral work. The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, physics informed machine learning, outlier detection, and unsupervised learning. Multiple positions are available with flexible start dates (not just Spring/Summer but throughout 2025) and duration (typically 3-6 months).

    • Research Areas: Artificial Intelligence, Speech & Audio, Machine Learning, Data Analytics
    • Host: Gordon Wichern
    • Apply Now