Internship Openings

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 is looking for a self-motivated intern to work on the problem of video anomaly detection. The intern will help to develop new ideas for improving the state of the art in detecting anomalous activity in videos. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision and some experience with video anomaly detection in particular. Proficiency in Python programming and Pytorch is necessary. 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, WACV, ICML, ICLR, NeurIPS or AAAI. The intern will collaborate with MERL researchers to develop and test algorithms and prepare manuscripts for scientific publications. The internship is for 3 months and the start date is flexible.

Required Specific Experience

• Graduate student in Ph.D. program
• Experience with PyTorch.
• Prior publication in computer vision or machine learning conference/journal.

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.

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 is looking for a highly motivated intern to work on an original research project in reconstruction/rendering dynamic 3D scenes. A strong background in 3D computer vision and/or computer graphics is required. Experience in the latest advances of deep learning in this area, such as neural radiance fields (NeRFs)/Gaussian Splatting (GS)/Point Map reconstruction methods, 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/graphics or machine learning venue, such as CVPR, ECCV, ICCV, SIGGRAPH, 3DV, ICML, ICLR, NeurIPS or AAAI, and possess solid programming skills in Python and popular deep learning frameworks like Pytorch. The goal would be for such a candidate to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The position is available for graduate students on a Ph.D. track or those that have recently graduated with a Ph.D. Duration and start dates are flexible but are expected to last for at least 3 months. This internship is preferred to be onsite at MERL’s office in Cambridge, MA.

Required Specific Experience

• Prior publications in top computer vision/graphics and/or machine learning venues, such as CVPR, ECCV, ICCV, SIGGRAPH, 3DV, ICML, ICLR, NeurIPS or AAAI.
• Experience in the latest novel-view synthesis approaches such as Neural Radiance Fields (NeRFs) or Gaussian Splatting (GS) and/or in the latest 3D point map reconstruction methods.
• Proficiency in coding (particularly scripting languages like Python) and familiarity with deep learning frameworks, such as PyTorch or Tensorflow.

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

We seek a highly motivated intern to conduct original research in generative models for instructional video generation. We are interested in applications to various tasks such as video generation from text, images, and diagrams. The successful candidate will collaborate with MERL researchers to design and implement novel models, conduct experiments, and prepare results for publication. The candidate should be a PhD student (or recent graduate) in computer vision and machine learning with a strong publication record including at least one paper in a top-tier computer vision or machine learning venue such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, AAAI, or TPAMI. Strong programming skills, experience developing and implementing new models in deep learning platforms such as PyTorch, and broad knowledge of machine learning and deep learning methods are expected, including experience in the latest advances in video generation. Start date is flexible; duration should be at least 3 months.

Required Specific Experience

• Experience with video diffusion models, physics simulators, and large vision-and-language models
• Experience developing and implementing new models in PyTorch
• At least one paper in a top-tier computer vision or machine learning venue such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, AAAI, or TPAMI.
• Ph.D. student in computer vision or a related field.

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 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.

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 seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus.

Required Specific Experience

• Experience with Python and Python Deep Learning Frameworks.
• Experience with FMCW radar and/or Depth Sensors.

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 on estimation algorithms for single-photon lidar. The ideal candidate would be a PhD student with a strong background in statistical modeling, estimation theory, computational imaging, and/or inverse problems. The intern will collaborate with MERL researchers to design new lidar reconstruction algorithms, conduct simulations, and prepare results for publication. A detailed knowledge of single-photon detection, lidar, and Poisson processes is preferred. Hands-on optics experience may be beneficial but is not required. Strong programming skills in Python or MATLAB are essential. The duration is anticipated to be 3 months with a flexible start date.

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.

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.