Internship Openings

MERL is looking for a self-motivated intern to research on the topic of language-guided dynamic human-robot interaction in simulations. The intern must have a strong background in state-of-the-art machine learning research including the knowledge of agentic AI technologies, toolboxes to train/fine-tune large vision-and-language models, as well as expertise working on simulation platforms such as AI Habitat or similar. 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 in realistic simulators, including AI Habitat, TDW, etc.
• Experience in modeling agentic pipelines for solving complex tasks, including assimilating multimodal data, natural language interaction, and physical reasoning.
• Strong computer vision and machine learning foundations, including reinforcement learning, training large vision-and-language models, etc.
• Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.)
• Must be enrolled in a graduate program, ideally towards a Ph.D.

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

MERL seeks a self-motivated graduate student to conduct research on Visual Simultaneous Localization and Mapping (V-SLAM). Depending on the candidate’s expertise and interests, the internship may focus on topics such as — but not limited to — camera pose estimation, feature detection and matching, visual-LiDAR data fusion, pose-graph optimization, loop closure detection, and image-based camera relocalization.

The ideal candidate is a PhD student with a strong foundation in 3D computer vision and proficient programming skills in C/C++ and/or Python. Applicants should have at least one publication in a premier computer vision, machine learning, or robotics conference, such as CVPR, ECCV, ICCV, NeurIPS, ICRA, or IROS.

The intern will collaborate with MERL researchers to develop and implement novel algorithms for V-SLAM, perform experiments, and document research outcomes. The work is expected to lead to a submission to a top-tier conference. The start date and internship duration are flexible.

Required Specific Experience

• Experience with 3D Computer Vision and Simultaneous Localization & Mapping (SLAM).

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

MERL seeks a motivated graduate student to conduct research in robust estimation for computer vision. Depending on the candidate’s background and interests, the internship may involve topics such as — but not limited to — camera pose estimation, 3D registration, camera calibration, pose-graph optimization, or transformation averaging.

The ideal applicant is a PhD student with strong expertise in 3D computer vision, RANSAC, or graduated non-convexity algorithms, along with solid programming skills in C/C++ and/or Python. Candidates should have at least one publication in a leading computer vision, machine learning, or robotics venue (e.g., CVPR, ECCV, ICCV, NeurIPS, ICRA, or IROS).

The intern will work closely with MERL researchers to develop and implement new algorithms for visual SLAM (V-SLAM), perform experiments, and document results. The goal is to produce work suitable for submission to a top-tier conference. The start date and duration of the internship are flexible.

Required Specific Experience

• Demonstrated experience in 3D computer vision, RANSAC, or graduated non-convexity algorithms for vision applications.

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

MERL is looking for a self-motivated intern to research on problems related to complex physical reasoning using digital twins and large vision-and-language models (VLMs). An ideal intern would be a Ph.D. student with a strong background in computer vision, machine learning, and robotics, with broad experience in using state-of-the-art physics engines. The candidate must have a strong background in 3D computer vision and machine learning (specifically in robotics and reinforcement learning), operational knowledge in using VLMs and generative AI, and experience in solving physical reasoning problems. Prior experience training VLMs would be a strong plus. The intern is expected to collaborate with researchers from multiple teams at MERL to develop algorithms and prepare manuscripts for scientific publications.

Required Specific Experience

• Experience with state-of-the-art physics simulators (both differentiable and non-differentiable)
• Experience in neuro-physical reasoning approaches
• Experience in state-of-the-art large vision-and-language models and generative AI models
• Enrolled in a PhD program
• Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

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.

MERL is seeking a highly motivated and qualified intern to contribute to the next generation of adaptive industrial robotics applications. The internship will focus on developing and validation of robust robotic disassembly solutions offering the opportunity to participate in cutting-edge research at the intersection of robotics, artificial intelligence, and sustainable manufacturing. There are several research topics of interest, including visual perception for disassembly guidance and planning, vision-language models for acting under uncertainty, and robot learning for manipulation. Applicants must be Ph.D. students with strong backgrounds in robot learning and computer vision. The selected intern will collaborate closely with MERL researchers to design and implement novel algorithms, conduct experiments, and disseminate research findings through a top-tier conference. The start date and duration are flexible, and interested applicants are encouraged to apply with an updated CV and a list of relevant publications.

Required Specific Experience

• Demonstrated experience in computer vision, robot learning, or vision-language models for robotic applications.

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 applying foundation models to manufacturing scenarios. The focus will be on leveraging large-scale pretrained models (e.g., vision-language models, multimodal transformers, diffusion policies) to specialize generalist manipulation policy to obtain high success rate in diverse but specific tasks. Potential research topics include few-shot policy learning, multimodal grounding of multiple sensor modalities to robot actions, and adapting foundation models for precise control and high success rate.

The ideal candidate will be a senior Ph.D. student with a strong background in machine learning for robotics, particularly in areas such as foundation models, imitation learning, reinforcement learning, and multimodal perception. Knowledge on large-scale Vision-Language-Action (VLA) and multimodal foundation models is expected. The internship will involve algorithm design, model fine-tuning, simulation experiments, and deployment on physical robot platforms equipped with cameras, tactile sensors, and force/torque sensors. The successful candidate will collaborate closely with MERL researchers, with the expectation of publishing in top-tier robotics or AI conferences/journals. Interested candidates should apply with an updated CV and relevant publications.

Required Specific Experience

• Strong background in machine learning for robotics, particularly foundation models (e.g., pi_0, OpenVLA, RT-X, etc.) and imitation learning.

• Experience with simulation environments such as Mujoco, Isaac Gym, or RLBench.

• Experience with physical robot platforms and sensors (vision, tactile, force/torque).

• Proficiency in Python, PyTorch, and modern deep learning frameworks

• Strong publication record in robotics, machine learning, or AI venues

Internship Details

• Duration: ~3 months
• Start Date: Summer 2026 (flexible based on mutual agreement)
• Goal: Publish research at leading robotics/AI conferences and journals

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.

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 a self-motivated and highly qualified Ph.D. intern to contribute to the development of a safety-oriented active SLAM system for aerial robots. The work will involve the development of perception-aware safe planning algorithms, along with extensive validation in both simulation and on hardware, using drones equipped with onboard cameras.

The intern will work closely with MERL researchers in robotics and autonomy. The internship is expected to lead to a publication in a top-tier robotics, computer vision, or control conference and/or journal. The position has a flexible start date (Summer/Fall 2026) and a duration of 3–6 months.

Required Specific Experience

• Current enrollment in a Ph.D. program in Mechanical Engineering, Electrical Engineering, Aerospace Engineering, Computer Science, or a closely related field, with a focus on Robotics, Computer Vision, and/or Control Systems.
• Hands-on experience with aerial robots, including real-world flight testing.
• Expertise in one or more of the following areas: active SLAM; 3D computer vision; coverage path planning; multi-agent pathfinding; perception-aware planning.
• Excellent programming skills in Python and/or C++, with prior experience using ROS2 and high-fidelity simulators such as Isaac Sim and/or MuJoCo.
• A strong publication record or demonstrated research potential in leading computer vision or robotics venues, such as ICRA, IROS, RSS, RA-L, T-RO, CVPR, ECCV, ICCV, or NeurIPS.

Preferred Experience

• Strong software engineering skills, demonstrated through a publicly accessible codebase (e.g., GitHub or GitLab). Applicants are required to provide links to representative repositories.
• Experience with onboard perception, visual-inertial systems, or safety-critical autonomy.
• Familiarity with trajectory optimization, MPC, or optimization-based control for robots.

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

MERL is seeking a highly motivated graduate student to develop high-fidelity full-stack GNC simulators for space applications. The ideal candidate has strong experience with rendering engines, synthetic image generation, and computer vision, as well as familiarity with spacecraft dynamics, motion planning, and state estimation. The developed software should allow for closed-loop execution with the synthetic imagery, and ideally allow for real-time visualization. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.

Required Specific Experience

• Current enrollment in a graduate program in Aerospace, Computer Science, Robotics, Mechanical, Electrical Engineering, or a related field

• Experience with one or more of Blender, Unreal, Unity, along with their APIs

• Strong programming skills in one or more of Matlab, Python, and/or C/C++

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