Internship Openings

MERL is seeking a motivated and qualified intern to conduct research on condition monitoring and fault diagnosis. The intern will contribute to the development of advanced monitoring and diagnostic technologies, with applications that may include electric motors and motor-driven systems. Ideal candidates should be Ph.D. students with a solid background and publication record in one or more of the following research areas: fault diagnosis, prognosis, and health management; electric machine modeling and data analysis; machine learning techniques including transfer learning and domain adaptation for fault diagnosis. Strong programming skills in Python and familiarity with frameworks such as PyTorch are required. Experience with modeling and analysis of electric machines is highly desirable. Senior Ph.D. students in related fields (e.g., Electrical Engineering, Mechanical Engineering, Applied Physics) are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.

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

Mitsubishi Electric Research Laboratories (MERL) is seeking a self-motivated Ph.D. candidate in Computer Science, Electrical Engineering, or a related field for a 3-month internship focused on developing advanced machine learning algorithms to fuse multi-modal time sequence data for electric machine condition monitoring and predictive maintenance. The ideal candidate will have a strong background in machine learning and signal processing with a proven publication record. Experience in time-sequence analysis, multimodal sensor fusion, or physics-informed machine learning is preferred. Knowledge of electric machines is a plus. The intern will collaborate with MERL researchers to design and develop novel algorithms, prepare technical reports, and contribute to manuscripts for top-tier scientific publications. This position requires onsite work at MERL, with a flexible start date.

Required Specific Experience

• Experience with multi-modal sensor fusion.

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

Join our cutting-edge research team to help advance the next generation of Embodied AI and Humanoid Robotics. As a research intern, you will develop AI technologies that enable humanoid robots to understand, reason, and interact with the physical world through complex manipulation, assembly, and tool-use tasks. This is a unique opportunity to contribute to impactful research with the goal of publishing at leading AI and robotics conferences.

What You'll Work On

Depending on your background and interests, projects may include:

• Embodied AI for dexterous manipulation, assembly, and tool use
• Vision-Language-Action (VLA) models and Foundation Models for robotic control
• World-Action Models (WAM) for long-horizon planning and decision making
• Learning from human demonstrations, teleoperation, and autonomous data collection
• Sim-to-real transfer, reinforcement learning, and real-world robot deployment

What We're Looking For

We are seeking highly motivated graduate students with:

• Strong research experience in robotics, embodied AI, machine learning, computer vision, or related fields
• Experience with deep learning frameworks such as PyTorch or JAX, and strong Python programming skills
• Familiarity with one or more of the following:
  • Vision-Language-Action (VLA) models
  • Foundation Models or multimodal AI
  • Reinforcement learning or imitation learning
  • Robot manipulation, motion planning, or control
  • Agentic AI systems for robotics

Preferred qualifications:

• Hands-on experience with humanoid or loco manipulators (e.g., Unitree G1)
• Experience with teleoperation systems (e.g., Pico, Sonic)
• Experience with robotics simulators (e.g., Isaac Sim, MuJoCo, Genesis)
• Familiarity with ROS/ROS 2 and real-world robot experimentation
• Familiarity with policy deployment on edge AI devices (e.g., Jetson GPUs)

Internship Details

• Duration: Approximately 4 months
• Start Date: Flexible
• Location: Cambridge, MA
• Objective: Conduct high-impact research leading to publications at premier AI and robotics conferences (e.g., CoRL, RSS, ICRA, IROS, NeurIPS, ICML)

If you are excited about building AI that enables robots to perform complex real-world tasks—including assembly, tool use, and dexterous manipulation—we encourage you to apply.

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

The Computational Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research on multi-modal sensing and understanding —algorithms that can understand, explain, and act on multi-sensor data (e.g., RF, infrared, LiDAR, event camera). Ideal candidates will be comfortable bridging state-of-the-art perception (detection/segmentation/tracking) with higher-level semantic understanding and reasoning capabilities. Experience with text, visual, and multimodal reasoning is a plus. The intern will work closely with MERL researchers to develop novel algorithms, design experiments using MERL’s in-house testbeds, and prepare results for patents and publication. The internship is expected to last 3 months, with a flexible start date.

Required Specific Experience

• Expertise in physical sensing across RF (radar, UWB, Wi-Fi), infrared, LiDAR, and event-camera modalities. Experienced with radar systems and concepts including FMCW and MIMO configurations, Doppler signature interpretation, radar point cloud and heatmap representations, and raw ADC waveforms;
• Solid understanding of state-of-the-art transformer-based (e.g., DETR) and diffusion-based (e.g., DiffusionDet) frameworks;
• Demonstrated work in text-, visual-, and multimodal semantic understanding and reasoning.
• Hands-on experience with open large-scale multi-sensor datasets (e.g., nuScenes, Waymo Open Dataset, Argoverse) and open radar datasets (e.g., MMVR, HIBER, RT-Pose, K-Radar).
• Proficiency in Python and deep learning frameworks (PyTorch/JAX), plus experience with GPU cluster job scheduling and scalable data pipelines.
• Proven publication record in top-tier venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML (or equivalent).

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

The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research on sensor reasoning models—algorithms that can understand, explain, and act on multi-sensor data (e.g., radar, infrared, LiDAR, event camera) through text-, visual-, and multimodal reasoning. Ideal candidates will be comfortable bridging modern perception (detection/segmentation/tracking) with higher-level reasoning capabilities. Experience with text, visual, and multimodal reasoning is highly preferred. The intern will work closely with MERL researchers to develop novel algorithms, design experiments using MERL’s in-house testbeds, and prepare results for patents and publication. The internship is expected to last 3 months, with a flexible start date from May 2027.

Required Specific Experience

• Reasoning with sensor data: Demonstrated work in text-, visual-, and multimodal reasoning (e.g., VQA over sensor streams, temporal/spatio-temporal reasoning, chain-of-thought, instruction following).
• LLMs & VLMs for sensor perception: Experience aligning or conditioning LLMs/VLMs on sensor outputs (e.g., point clouds, radar heatmaps, BEV features).
• Perception foundations: Solid understanding of state-of-the-art transformer-based and diffusion-based frameworks
• Datasets & evaluation: Hands-on experience with open large-scale multi-sensor datasets (e.g., OctoNet, CUHK-X) and open radar datasets (e.g., MMVR, HuPR, HIBER, RT-Pose, K-Radar). Ability to design reasoning-centric benchmarks.
• Proficiency in Python and deep learning frameworks (PyTorch/JAX), plus experience with GPU cluster job scheduling and scalable data pipelines.
• Proven publication record in top-tier venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML (or equivalent).
• Knowledge of sensor (RF, infrared, LiDAR, event camera) fundamentals; for radar, familiarity with FMCW, MIMO, Doppler signatures, radar point clouds/heatmaps, and raw ADC waveforms.
• Familiarity with MERL’s recent radar perception research, e.g., TempoRadar, SIRA, MMVR, RETR, RAPTR, REXO.

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

We are seeking graduate students interested in advancing the fields of spatial audio, room acoustics, physics informed machine learning, and scene understanding (e.g., sound source localization, detection, and captioning). The interns will work closely with MERL researchers to develop novel algorithms, measure data, 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 PhD students with experience in some of the following: audio signal processing, microphone array processing, physics informed machine learning, and 3D modeling in computer vision. The expected starting date is early 2027.

Required Specific Experience

• Experience with deep learning frameworks (e.g., PyTorch and JAX)
• Research experience in audio signal processing and/or array signal processing

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

We are seeking graduate students interested in advancing acoustic metamaterials and active noise control technologies for reducing machine noise and improving acoustic privacy. The interns will collaborate with MERL researchers to develop novel approaches for controlling sound propagation through a combination of passive acoustic structures and active sound field control. Research may involve the design and optimization of acoustic metamaterials, microphone and loudspeaker array processing, adaptive noise cancellation, and experimental validation of noise reduction and speech privacy systems. The interns will develop and implement algorithms, conduct acoustic simulations and measurements, build experimental prototypes, and prepare results for publication.

The ideal candidates are Ph.D. students with experience in some of the following: acoustics, acoustic metamaterials, active noise control, adaptive filtering, acoustic wave propagation, microphone and loudspeaker arrays, numerical simulation, and signal processing. Experience with finite element modeling, optimization, or physics-informed machine learning is also desirable.

Flexible start dates and internship durations (typically 3–6 months) are available.

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