Internship Openings

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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.

In addition to base pay, interns receive a relocation stipend, covered travel to and from MERL, and a monthly Charlie Card for local commuting. Interns are invited to participate in weekly social gatherings and professional development opportunities, including research talks by both internal and external speakers. Interns who meet the 90-day waiting period are also eligible for health insurance coverage. MERL provides immigration support for qualified candidates as needed. Employment is considered "at-will," and the Company reserves the right to modify base salary or any other compensation program at any time, including for reasons related to individual performance, departmental or Company performance, and market conditions.


  • CI0197: Internship - Embodied AI & Humanoid Robotics

    • Those who are passionate about pushing the boundaries of embodied AI, join our cutting-edge research team as an intern and contribute to the development of generalist AI agents for humanoid robots. This is a unique opportunity to work on impactful projects aimed at publishing in top-tier AI and robotics venues.

      What We’re Looking For

      We’re seeking highly motivated individuals with:

      • Advanced research experience in robotic AI, edge AI, and agentic AI systems.
      • Hands-on expertise in Vision-Language-Action (VLA) models and Foundation Models
      • Strong proficiency with Python, PyTorch/JAX, deep learning, and robotic agent frameworks

      Internship Details

      • Duration: ~3 months
      • Start Date: Flexible
      • Goal: Publish research at leading AI/robotics conferences and journals

      If you're excited about shaping the future of humanoid robotics and AI agents, we’d love to hear from you!

      The pay range for this internship position will be 6-8K per month.

    • Research Areas: Applied Physics, Artificial Intelligence, Computer Vision, Control, Machine Learning, Robotics, Signal Processing, Speech & Audio, Optimization
    • Host: Toshi Koike-Akino
    • Apply Now

  • ST0242: Internship - Radiation Detection and Estimation

    • MERL is seeking a motivated intern to support a research project focused on detecting and estimating properties of radiation sources (e.g., gamma, beta, alpha). The Computational Sensing team is developing new estimation and inference methods to analyze sensor data from radiation detection systems. To enable this, we require realistic physics-based simulations of particle transport and sensor interactions. The primary goal of this internship is to develop and validate high-fidelity radiation simulations using the Geant4 toolkit, providing data and visualization tools that will accelerate our algorithm development and testing. The ideal candidate would be a PhD student with experience in detector modeling and a familiarity with data analysis tools such as NumPy or ROOT. An understanding of inverse problems or estimation techniques may be beneficial but is not required. The duration is anticipated to be 3 - 6 months with a flexible start date.

      Required Specific Experience

      • Strong background in nuclear physics, radiation detection, or high-energy physics.
      • Demonstrated expertise with Geant4 (geometry setup, physics lists, scoring, visualization).

      The pay range for this internship position will be 6-8K per month.

    • Research Areas: Applied Physics, Computational Sensing, Electronic and Photonic Devices, Signal Processing
    • Host: Joshua Rapp
    • Apply Now

  • ST0247: Internship - Geometry-Aware Surrogate Modeling for Fluid Dynamics

    • The Computational Sensing team at MERL is seeking a highly motivated Ph.D. student for a research internship in machine learning for fluid dynamics, focusing on surrogate modeling of free-surface flows in engineered geometries. The goal of this project is to develop geometry-aware and physics-informed surrogate models for complex flow systems, combining high-fidelity simulations with modern neural architectures. The ideal candidate will be a Ph.D. student in engineering, applied mathematics, computer science, or related fields with a solid background and publication record in any of the following areas: operator learning, graph neural networks and geometric learning, particle-based methods, or differentiable simulation frameworks. Programming skills in Python and experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX are required. Publication of the results obtained during the internship is expected. The duration is anticipated to be at least 3 months with a flexible start date.

      The pay range for this internship position will be $6-8K per month.

    • Research Areas: Applied Physics, Artificial Intelligence, Computational Sensing, Dynamical Systems, Machine Learning
    • Host: Wael Ali
    • Apply Now

  • ST0184: Internship - Uncertainty Quantification & Bayesian Inverse Problems

    • The Computational Sensing team at MERL is seeking a highly motivated PhD student for an internship focused on uncertainty quantification (UQ) in computational modeling of physical systems. The goal of this project is to advance the methodology and practice of UQ, with a focus on generative models, reduced-order stochastic models, and optimal sensor placement for Bayesian inverse problems. The research will draw upon foundational ideas and techniques in applied mathematics and statistics for applications in wave propagation, fluid dynamics, and more generally high-dimensional systems. The ideal candidate will be a PhD student in engineering, applied mathematics, computer science, or related fields with a solid background and publication record in any of the following areas: generative models, stochastic modeling, dimensionality reduction, Bayesian inference, optimal experimental design, and tensor methods. Programming skills in Python or MATLAB are required. Publication of the results obtained during the internship is expected. The duration is anticipated to be at least 3 months with a flexible start date.

      The pay range for this internship position will be $6-8K per month.

    • Research Areas: Computational Sensing, Dynamical Systems, Applied Physics, Machine Learning, Optimization
    • Host: Wael Ali
    • Apply Now