Internship Openings

Join our cutting-edge research team to help advance the next generation of Embodied AI and Humanoid Robotics. As a research intern, you will develop AI technologies that enable humanoid robots to understand, reason, and interact with the physical world through complex manipulation, assembly, and tool-use tasks. This is a unique opportunity to contribute to impactful research with the goal of publishing at leading AI and robotics conferences.

What You'll Work On

Depending on your background and interests, projects may include:

• Embodied AI for dexterous manipulation, assembly, and tool use
• Vision-Language-Action (VLA) models and Foundation Models for robotic control
• World-Action Models (WAM) for long-horizon planning and decision making
• Learning from human demonstrations, teleoperation, and autonomous data collection
• Sim-to-real transfer, reinforcement learning, and real-world robot deployment

What We're Looking For

We are seeking highly motivated graduate students with:

• Strong research experience in robotics, embodied AI, machine learning, computer vision, or related fields
• Experience with deep learning frameworks such as PyTorch or JAX, and strong Python programming skills
• Familiarity with one or more of the following:
  • Vision-Language-Action (VLA) models
  • Foundation Models or multimodal AI
  • Reinforcement learning or imitation learning
  • Robot manipulation, motion planning, or control
  • Agentic AI systems for robotics

Preferred qualifications:

• Hands-on experience with humanoid or loco manipulators (e.g., Unitree G1)
• Experience with teleoperation systems (e.g., Pico, Sonic)
• Experience with robotics simulators (e.g., Isaac Sim, MuJoCo, Genesis)
• Familiarity with ROS/ROS 2 and real-world robot experimentation
• Familiarity with policy deployment on edge AI devices (e.g., Jetson GPUs)

Internship Details

• Duration: Approximately 4 months
• Start Date: Flexible
• Location: Cambridge, MA
• Objective: Conduct high-impact research leading to publications at premier AI and robotics conferences (e.g., CoRL, RSS, ICRA, IROS, NeurIPS, ICML)

If you are excited about building AI that enables robots to perform complex real-world tasks—including assembly, tool use, and dexterous manipulation—we encourage you to apply.

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.

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 seeking a highly motivated intern to collaborate on the development and experimental validation of algorithms for heterogeneous mobile robot autonomy. The internship will involve hands-on work with multiple robotic platforms, including aerial robots and ground robots. The ideal candidate should be comfortable working close to hardware and should have substantial experience with ROS2-based robotic systems, Python development, sensor integration, debugging physical experiments, and deploying algorithms on real robots. The intern will contribute to one or more research directions involving perception-driven autonomy, multi-robot planning, and heterogeneous robot coordination. The results of the internship are expected to lead to publications in top-tier robotics, control, automation, and/or computer vision conferences and/or journals. The internship will take begin in November/December 2026 and continue for 4–6 months, with exact dates flexible. Please use your cover letter to explain how you meet the following requirements. Where possible, include links to papers, code repositories, hardware demonstrations, videos, project pages, or prior experimental work that demonstrate your experience.

Required Specific Experience

• Current enrollment in a Masters/PhD program in Mechanical, Electrical Engineering, Computer Science, or related programs, with a focus on Robotics and/or Control Systems.
• Strong hands-on experience with robotic hardware and physical experiments.
• Experience in one or more of the following topics: multi-agent planning and control, perception, computer vision, optimization and operation research (vehicle routing and scheduling).
• Experience with one or more of ROS2-enabled mobile robots, preferably Starling drones, Crazyflie drones, TurtleBot / TurtleBot4 platforms, and/or Unitree Go2.
• Strong programming skills in Python and/or C/C++.

Desired Specific Experience

• Experience with image-based 3D reconstruction, structure from motion, visual SLAM, visual odometry, multi-view geometry, or photogrammetry.
• Experience with multi-robot coordination, heterogeneous robot teams, task allocation, dynamic team formation, coverage, monitoring, or task completion.
• Experience with motion planning, trajectory optimization, model predictive control, convex optimization, or mixed-integer optimization.

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 seeks a motivated graduate student to develop multi-fidelity dynamic simulation methods for energy systems (e.g., vapor-compression/HVAC cycles and related multiphysics platforms). Candidates should have hands-on time-domain numerical simulation experience (ODE/DAE integration, implicit/iterative solvers, sparse linear algebra), familiarity with model reduction or surrogate modeling, solid thermofluids literacy (thermodynamics, heat transfer, fluid mechanics), and strong programming skills in Python/Julia/Matlab. System identification and/or numerical optimization for dynamical systems, and familiarity with equation-oriented tools (Modelica or Simscape), are desirable; a track record of rigorous research (papers or robust software) is preferred. Senior PhD students in applied mathematics, chemical/mechanical engineering, or related areas are encouraged to apply. The internship is 3 months, with a flexible start date.

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 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.