Internship Openings

The Computational Sensing team at MERL is seeking a highly motivated Ph.D. student for a research internship in AI-aided fluid modeling and engineering design. The goal is to develop reasoning-driven and self-improving methods that use simulation feedback to discover and refine physical models, computational algorithms, and engineering designs. Potential directions include automated experimentation, iterative program improvement, and recursive self-improvement of modeling and optimization strategies. The ideal candidate will be a Ph.D. student in engineering, applied mathematics, computer science, or related fields with a solid background and publication record in one or more of the following areas: foundation models and agentic AI, automated algorithm discovery and refinement, scientific machine learning, computational fluid dynamics, or engineering optimization. Programming skills in Python and experience with machine learning frameworks or scientific computing tools are required. Publication of the results obtained during the internship is expected. The duration is anticipated to be at least 3 months with a flexible start date.

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

The Computational Sensing team at Mitsubishi Electric Research Laboratories (MERL) is seeking a highly motivated PhD student for a research internship focused on generative modeling, probabilistic inference, and optimization. Potential research directions include diffusion and flow-based generative models, efficient and controllable generation, Bayesian inference and uncertainty quantification, and generative methods for optimization. Applications may include materials and molecular discovery, inverse design, imaging, and modeling of complex physical and dynamical systems. Specific research topics will be developed collaboratively based on mutual interests and expertise. We particularly value creativity, strong mathematical and computational skills, independent thinking, and complementary expertise across disciplines. Publication of research results is expected. The internship is anticipated to last at least 3 months, with a flexible start date.

Specific Requirements:

• PhD student in computer science, applied mathematics, statistics, computational chemistry, materials science, engineering, or a related field.

• Strong research background in one or more of the following areas: generative modeling, probabilistic machine learning, stochastic processes, optimization, or computational modeling of physical and molecular systems.

• Proficiency in Python and experience with modern machine learning frameworks or scientific computing tools.

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 fast electromagnetic transient (EMT) simulation of data-center power systems. The ideal candidate will have a strong background in one or more of the following areas: data-center power systems, power-system dynamics, AI load dynamics, EMT simulation, and power electronic converters and controls. Proficiency in Python, MATLAB, or C++ programming is required. Ph.D. students in electrical engineering or related fields are encouraged to apply. The internship is typically 3–6 months in duration, with a flexible start date.

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

We are seeking a motivated research intern to join the optimization research team at MERL. The internship will focus on developing novel algorithms for challenging mathematical optimization problems, particularly in mixed-integer programming. Potential research directions include efficient reoptimization methods, algorithm acceleration, and applications to industrial optimization problems. We are looking for candidates with a strong background in mathematical optimization and an interest in both theoretical and computational research.

Required Specific Experience

• Strong background in mathematical optimization, particularly linear and mixed-integer programming.

• Experience with mathematical modeling, programming in Python and/or C++, and optimization solvers (e.g., Gurobi).

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

Mitsubishi Electric Research Laboratories (MERL) is seeking a highly motivated Ph.D. student to develop hybrid learning and control methods for reliable humanoid manipulation in factory automation. The research will combine Vision-Language-Action (VLA) models and World-Action Models (WAMs) with classical motion planning, feedback control, and perception. A central question is how robots should coordinate these components, recognize their limitations, and recover when execution deviates from the intended task.

The intern will develop and evaluate novel algorithms on physical robotic platforms, targeting industrial tasks such as container stacking and transport, kitting, tool manipulation, and assembly/disassembly. The project will combine algorithm development with simulation and real-world experiments, comparing approaches through task success, recovery performance, execution time, and constraint violations. The intern will work closely with MERL researchers on a focused research contribution, with the goal of submitting findings to leading robotics or machine-learning conferences such as CoRL, RSS, ICRA, IROS, NeurIPS, or ICML.

Required Specific Experience

• Current enrollment in a Ph.D. program in robotics, computer science, mechanical engineering, electrical engineering, or a related field.
• Hands-on experience adapting or evaluating VLA models, such as GR00T, p0.5, or related models, including deployment on physical robots.
• Strong foundations in robot manipulation, motion planning, and feedback control.
• Proficiency in Python, ROS2, and a deep-learning framework such as PyTorch.
• Experience conducting robotic experiments using simulation environments such as Isaac Sim or MuJoCo.
• Demonstrated research ability through publications, substantial research projects, or research software.

Preferred Experience

• Humanoid (Unitree G1, Agibot G2) or bimanual manipulation, including integration with whole-body control.
• World-action models or learned world models for prediction, planning, or control.
• Learning from demonstrations, teleoperation, or autonomous data collection.
• Uncertainty estimation, failure detection, policy intervention, or sim-to-real transfer.
• Contributions to open-source robotics or robot-learning repositories.

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

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.

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.