Internship Openings

Efficient Foundation Models for Edge Intelligence

We are seeking passionate and skilled interns to join our cutting-edge research team at Mitsubishi Electric Research Laboratories (MERL), focusing on efficient and sustainable AI. This internship offers a unique opportunity to contribute to next-generation machine learning techniques that enable real-time, edge, and energy-efficient AI systems — with the ultimate goal of publishing at top-tier AI venues.

Research Focus Areas

• Edge AI, real-time AI, and compact neural architectures
• Energy-efficient and hardware-friendly AI
• On-device, on-premise, and embedded-system AI
• Generative and multi-modal foundation models with resource constraints

Qualifications

• Advanced research experience in generative models, efficient architectures, or foundation models (LLM, VLM, LMM, FoMo)
• Strong understanding of state-of-the-art machine learning and optimization techniques
• Proficiency in Python and PyTorch, with familiarity in other deep learning frameworks
• Proven research record and motivation for publication in leading AI conferences

Internship Details

• Duration: Approximately 3 months
• Start Date: Flexible
• Objective: Conduct high-quality research leading to publications in premier AI conferences

If you are a highly motivated researcher eager to push the boundaries of efficient and sustainable AI, we encourage you to apply. Join us in shaping the future of intelligent systems that are not only powerful but also responsible and sustainable.

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

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.

MERL is seeking passionate and skilled research interns to join our team focused on developing artificial intelligence technologies realizing more capable, general, trustworthy, and efficient agents. This is an exciting opportunity to make an impact on the field of agentic AI, 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 robustness, trustworthiness, and/or more capable agents.
• 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 agentic AI 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.

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

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.

The Computational Sensing Team at MERL is seeking an intern to work with MERL researchers on algorithms based on interacting particle systems for solving inverse problems. The focus of the project is particle-efficiency and applicability to non-log-concave posterior distributions (which may result from nonlinear forward operators). The project includes algorithm design, (finite-particle) convergence analysis, and/or empirical evaluation for challenging inverse problems such as full waveform inversion. The ideal candidate would be a PhD student with a solid background in applied probability or Bayesian sampling. Programming skills in Python or MATLAB are required. 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.

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.

MERL is looking for a highly motivated intern to work on an original research project on audio-visual learning, with a focus on spatial audio, training models using limited labeled data. A strong background in computer vision, audio processing, and deep learning is required. Experience in audio-visual (multimodal) learning, weakly/self-supervised learning, Room Impulse Response (RIR) estimation, and large (vision-) language models is an added plus and will be valued. 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 solid programming skills in Python and popular deep learning frameworks such as Pytorch. The intern will collaborate with MERL researchers to develop and implement novel algorithms and prepare manuscripts for scientific publications. Successful applicants are typically graduate students on a Ph.D. track or recent Ph.D. graduates. Duration and start date are flexible, but the internship is expected to last for at least 3 months.

Required Specific Experience

• Prior publications in top-tier computer vision and/or machine learning venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI.
• Knowledge of the latest self-supervised and weakly-supervised learning techniques.
• Experience with Large (Vision-) Language Models, Spatial audio processing techniques.
• Proficiency in scripting languages, such as Python, and deep learning frameworks such as PyTorch or Tensorflow.

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

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MERL is seeking a highly motivated intern to conduct original research in estimating vital signs such as heart rate, heart rate variability, blood pressure, and blood oxygen level 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.

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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.

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