Internship Openings

MERL is looking for a self-motivated intern to research on problems at the intersection of multimodal large language models and neural algorithmic reasoning. An ideal intern would be a Ph.D. student with a strong background in machine learning and computer vision. The candidate must have prior experience with training multimodal LLMs for solving vision-and-language tasks. Experience in participating and winning mathematical Olympiads is desired. Publications in theoretical machine learning venues would be a strong plus. The intern is expected to collaborate with researchers in the computer vision team at MERL to develop algorithms and prepare manuscripts for scientific publications.

Required Specific Experience

• Experience with training large vision-and-language models
• Experience with solving mathematical reasoning problems
• Experience with programming in Python using PyTorch
• Enrolled in a PhD program
• Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

MERL is looking for a self-motivated intern to work on problems at the intersection of multimodal large language models and embodied AI in dynamic indoor environments. The ideal candidate would be a PhD student with a strong background in machine learning and computer vision, as demonstrated by top-tier publications. The candidate must have prior experience in designing synthetic scenes (e.g., 3D games) using popular graphics software, embodied AI, large language models, reinforcement learning, and the use of simulators such as Habitat/SoundSpaces. Hands on experience in using animated 3D human shape models (e.g., SMPL and variants) is desired. The intern is expected to collaborate with researchers in computer vision at MERL to develop algorithms and prepare manuscripts for scientific publications.

Required Specific Experience

• Experience in designing 3D interactive scenes
• Experience with vision based embodied AI using simulators (implementation on real robotic hardware would be a plus).
• Experience training large language models on multimodal data
• Experience with training reinforcement learning algorithms
• Strong foundations in machine learning and programming
• Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

We are seeking graduate students interested in helping advance the understanding of applying sophisticated audio processing techniques (e.g., source separation, localization, anomalous sound detection) to moving sound sources (e.g., vehicles). The interns will collaborate with MERL researchers to derive and implement novel algorithms, record data, 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, source separation, physics informed machine learning, outlier detection, and unsupervised learning.

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

MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems for process applications. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in control and estimation, numerical methods, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 3 months.

MERL is seeking a highly motivated and qualified intern to conduct research on decentralized data assimilation for multi-physical and multi-component systems governed by large-scale nonlinear differential-algebraic equations (DAEs). The research will focus on the study, development, and efficient implementation of data assimilation algorithms for such complex systems. The ideal candidate will have a strong background in one or more of the following areas: nonlinear estimation and control, Bayesian methods, machine learning, graph theory, and optimization, with demonstrated expertise through peer-reviewed publications or equivalent experience. Proficiency in Julia or Python programming is required. Senior Ph.D. students in mechanical, electrical, chemical engineering, or related fields are encouraged to apply. The internship is typically 3 months in duration, with a flexible start date.

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

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.

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.

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.

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.