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.

MERL is seeking a highly motivated and qualified PhD student to conduct research on privacy-preserving and secure agentic AI systems. The aim of the internship to collaborate with MERL researchers on developing novel fundamental technologies that enhance the privacy and security of agentic systems that employ foundation models, such as LLMs/VLMs. The goal is to publish new scientific results at top-tier AI research conferences.

Required Specific Experience

• Currently pursuing a PhD in Computer Science, Electrical Engineering, or a related field.
• Strong background in machine learning, LLMs/VLMs, foundation models, and agentic AI systems.
• Research experience with private and/or secure foundation models (e.g., differential privacy, adversarial inputs, jailbreaking attacks/defenses, prompt injection).
• Proficiency in Python and deep learning frameworks, such as PyTorch and Hugging Face tools.
• Proven publication record in top-tier ML/AI research conferences.

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

MERL is seeking a highly motivated and qualified intern to conduct research on multi-hop IoT network anomaly detection and analysis. The candidate is expected to develop innovative network anomaly detection technologies that can proactively detect and analyze network failure in multi-hop IOT networks. The candidate should have knowledge of LLM/ML and anomaly detection. Knowledge of network log analysis and network protocol a plus. Start date for this internship is flexible and the duration is about 3 months.

Responsibilities for this position include:

• Research on anomaly detection in multi-hop IoT networks
• Develop innovative network anomaly detection and analysis technologies
• Simulate and analyze the performance of developed technology

Qualifications for this position are:

• Junior and senior year Ph.D candidates

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

PRINCIPALS ONLY. No phone calls please.

Mitsubishi Electric Research Laboratories, Inc. is an Equal Opportunity Employer.

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 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, while experience in multi-modal data analysis or domain adaptation is preferred and knowledge of electric machines is a plus. The intern will collaborate with MERL researchers to design and develop novel machine learning 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 Python and Matlab.

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 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.

The pay range for this internship position will be6-8K per month.

We are looking for a graduate student interested in advancing the field of multimodal scene understanding, focusing on scene understanding using natural language for robot dialog and/or indoor monitoring with 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 a flexible start date (not just Spring/Summer but throughout 2026) and duration (typically 3-6 months).

Required Specific Experience

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

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

We are seeking graduate students interested in helping advance the fields of machine sound source separation, sound event detection/localization, anomaly detection, and physics informed deep learning for machine sounds in extremely noisy conditions. 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, audio source separation (music, speech, or general sounds), microphone array processing, sound event localization and detection, anomaly detection, and physics informed machine learning.

Multiple positions are available with flexible start dates (not just Spring/Summer but throughout 2026) and duration (typically 3-6 months).

The pay range for this internship position will be6-8K per month.

We are seeking graduate students interested in helping advance the fields of generative audio, source separation, speech enhancement, 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, probabilistic modeling, and deep generative modeling.

Multiple positions are available with flexible start dates (not just Spring/Summer but throughout 2026) and duration (typically 3-6 months).

MERL is looking for a highly motivated and qualified intern to work on methods for task-oriented 6-dof grasp pose detection using vision and tactile sensing. The objective is to enable a robot to identify multiple 6-DoF grasp poses tailored to specific tasks, allowing it to effectively grasp and manipulate objects. The ideal candidate would be a Ph.D. student familiar with the state-of-the-art methods for robotic grasping, object tracking, and imitation learning. This role involves developing, fine-tuning and deploying models on hardware. The successful candidate will work closely with MERL researchers to develop and implement novel algorithms, conduct experiments, and publish research findings at a top-tier conference. Start date and expected duration of the internship is flexible. Interested candidates are encouraged to apply with their updated CV and list of relevant publications.

Required Specific Experience

• Prior experience in robotic grasping
• Experience in Machine Learning
• Excellent programing skills

The pay range for this internship position will be6-8K per month.

MERL is looking for a highly motivated and qualified PhD student in the areas of machine learning and robotics, to participate in research on advanced algorithms for learning control of robots and other mechanisms. Solid background and hands-on experience with various machine learning algorithms, including deep learning, is expected, as well as good understanding of computer vision methods, in particular algorithms for keypoint detection and tracking. Exposure to deep reinforcement learning and/or learning from demonstration is highly desirable. Familiarity with the use of machine learning algorithms for system identification of mechanical systems would be a plus, along with background in other areas of automatic control. Familiarity with visual servocontrol is highly desirable. Solid experimental skills and hands-on experience in coding in Python, PyTorch, and OpenCV are required for the position. Some experience with ROS2 and familiarity with classical mechanics and computational physics engines would be helpful, but is not required. Hands-on familiarity with industrial robots will be a definite plus. The position will provide opportunities for exploring fundamental problems in incremental learning in humans and machines, leading to publishable results. The duration of the internship is 3 to 5 months. Preference will be given to candidates who can start no later than the beginning of January 2025.

Required Specific Experience

• Python, PyTorch, OpenCV
• Keypoint tracking in images

Desired Specific Experience

• Visual servocontrol of robots
• Learning diffusion policies
• MuJoCo or other physics engines
• System identification
• Clustering algorithms
• ROS2

The pay range for this internship position will be6-8K 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., RF, 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 October 2025 onward.

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 (e.g., DETR) and diffusion-based (e.g., DiffusionDet) frameworks
• Datasets & evaluation: 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). Ability to design reasoning-centric benchmarks (e.g., QA over multi-sensor inputs, temporal prediction).
• 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.

The pay range for this internship position will be6-8K per month.

The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that can perform background oriented schlieren (BOS) tomography. The project goal is to utilize both analytical and learning-based architectures to enable the reconstruction of 3D air flows in an indoor setting from BOS measurements coupled with physics informed machine learning. 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, differentiable scene rendering, learning-based modeling for imaging, and physics informed neural networks. Preferred skills include experience with schlieren tomography, inverse rendering, neural scene representation, computational imaging hardware, and computationally efficient optimization of PINNs. 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.

Required Specific Experience

• Experience with differentiable/physics-based rendering.

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