Internship Openings

MERL is seeking a highly motivated and qualified intern to conduct research on applying foundation models to robotic manipulation. The focus will be on leveraging large-scale pretrained models (e.g., vision-language models, multimodal transformers, diffusion policies) to enable generalist manipulation capabilities across diverse objects, tasks and embodiments including humanoids. 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. Experience in working with humanoids is

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.

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.

We seek a talented individual for a joint research project in AI-augmented optimization: Using LLMs and related technologies to aid in the formulation, transformation, and accelerated solution of typical Operations Research optimization problems. Depending on intern skill set, the project may also consider post-optimization enhancements — explanation, robustification, and generation of alternatives. The ideal applicant will have technical background and research experience on both sides (AI & OR) of the project, including familiarity with software environments used to do machine learning & mathematical programming. The internship has flexible dates in 2026.

The strongest candidates will have:

• A solid background in machine learning, linear algebra, convex optimization, algorithm analysis, computational geometry.
• Experience with Mixed-Integer Programs (linear, nonlinear, nonconvex)
• Experience deploying and fine-tuning LLM-type AI systems.
• Competence with related software packages such as Gurobi, CVX, PyOpt, PyTorch, etc.
• Fluency in python.
• Interest (& experience) in publishing in top-tier venues.

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

MERL's OR group is seeking a talented individual to collaborate in an ongoing research into solving Mixed-Integer Quadratic Programs via continuous dual formations, e.g., co-positive programs.

The ideal application will have:

• Experience with Mixed-Integer Programs (preferably all of: linear, nonlinear, nonconvex)
• Competence with related software packages such as Gurobi, CVX, PyOpt, etc.
• Fluency in python.
• A solid background in linear algebra, convex optimization, algorithm analysis, computational geometry.

The internship will run 3-4 months and is available immediately; applicants with early 2026 availability will be given favorable consideration in hiring.

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

MERL is looking for a research intern to develop efficient transformer-informed stochastic MPC to control net-zero energy buildings. This is an exciting opportunity to make a real impact in the field of cutting-edge deep learning and predictive control on a real system. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.

The Ideal Candidate Will Have:

• Significant hands-on experience with stochastic MPC
  • Publications in SMPC are a strong plus
• Fluency in Python and PyTorch
• Understanding of probabilistic time-series prediction
• Experience with convex programming
  • Convex formulations of MPC/SMPC problems are a strong plus
• Completed their MS, or >50% of their PhD program

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 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 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.

MERL is seeking a highly motivated intern to collaborate in the development decision making, planning and control for teams of heterogeneous robots (aerial, ground wheeled, legged etc.) in task such as inspection, monitoring and infrastructure repair. The ideal candidate is a PhD student with strong experience in planning and control of multi-agent systems, with background in advanced model-based (e.g., MPC) and learning-based (e.g., RL) methods. The results of the internship are expected to be published in top-tier conferences and/or journals. The internship will take place during Spring/Summer 2026 (exact dates are flexible) with an expected duration of 3-6 months.

Please use your cover letter to explain how you meet the following requirements, preferably with links to papers, code repositories, etc., indicating your proficiency.

Required Experience

• Current enrollment in a PhD program in Mechanical, Electrical, Aerospace Engineering, Computer Science or related programs, with a focus on Robotics and/or Control Systems
• Experience in as many as possible of:
  • Formal methods and set based methods (temporal logics, reachability, invariance)
  • Model predictive control (design, analysis, solvers)
  • Reinforcement learning for planning
  • Cooperative planning and control for multi-agent systems
  • Programming in Python or Matlab or Julia

Additional Useful Experience

• Knowledge of one or more physics simulators for robotics (e.g., MuJoco)
• Experience with coverage control and pursuit-evasion problems
• Programming in C/C++ or Simulink code generation

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.

MERL is seeking a motivated and qualified individual to conduct research in analysis and optimization of hybrid vehicles. The ideal candidate should have solid backgrounds in hybrid electrical propulsion system modeling and analysis, optimization, and optimal control. Excellent coding skills on MATLAB and/or python is a necessity. Ph.D. students in aerospace and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

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

MERL is seeking a highly motivated and qualified individual to conduct research in differentiable constraint modeling and optimal control of high performance motion systems. The ideal candidate should have solid backgrounds in sensitivity analysis, reachability analysis, and optimal control. A proven record of publishing results in leading conferences/journals is necessary. Demonstrated experience of using dSPACE for real-time HIL experimentation is a plus. Ph.D. students in electrical engineering, control, and related areas are encouraged to apply. Start date for this internship is around summer 2026 and the duration is about 3 months.

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 shape and topology optimization. The intern will contribute to the development of topology optimization algorithms for engineering design problems, with applications that may include electromagnetic devices and mechanical structures. Ideal candidates should have a solid background and demonstrated research experience in mathematical optimization methods, including topology optimization, robust optimization, sensitivity analysis, and machine learning-based optimization. Hands-on coding experience in implementing topology optimization algorithms and performing finite-element simulation are desirable. Knowledge and experience with electric machine principle, design and finite-element analysis is a strong plus. 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.

MERL is seeking a highly motivated and qualified individual to conduct research on fast/robust whole-body motion planning and control of mobile manipulators for agility, safety and precision. The ideal candidate should demonstrate solid background and track record of publications in the areas of robotic dynamics, motion planning, and control. Strong C++ and Python coding skills, knowledge of robotic software such as Pinocchio/Pybullet/MuJoCo, and optimization tools such as CasADi/PyTorch are a necessity. Ph.D. students in mechanical engineering, robotics, computer science, and electrical engineering are encouraged to apply. Start date for this internship is around summer 2026 and the duration is about 3 months.

Required Specific Experience

• Experience with robotic software such as Pinocchio/Pybullet/MuJoCo/ROS
• Strong C++ and Python coding skills
• Optimization tools such as CasADi/PyTorch

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

MERL is seeking a highly motivated and qualified individual to conduct research in the sample efficient safe reinforcement learning via the integration of model- and learning-based theories, with applications to ultra-high precision positioning systems. The ideal candidate should have solid backgrounds in dynamical systems, control theory and reinforcement learning, and strong coding skills. Prior experience on ultra-high precision motion control systems is a plus. Ph.D. students in learning and control are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

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.