Internship Openings

MERL is seeking a highly motivated and qualified intern to carry out research on UAV assisted IoT network methodology. The candidate is expected to develop innovative path planning technologies to support UAV swarm navigation in IoT network environments. The candidates should have knowledge of communication network technologies such as path planning and cooperative network operations. Knowledge of control technology and path management is a plus. Start date for this internship is flexible and the duration is about 3 months.

Responsibilities for this position include:

• Research on UAV assisted IoT networks
• Develop path planning technologies to support UAV coordination in IoT networks
• Simulate and analyze the performance of developed technology

Qualifications for this position are:

• Junior and senior year Ph.D students

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 intern to conduct research on electromagnetic transient analysis of power grids. The ideal candidate will have a strong background in power systems, transient analysis, dynamic simulation, converter control, numerical methods, and optimization. Proficiency in MATLAB, Python, or C++ is required. Prior experience with electromagnetic transient analysis will be considered a plus. This internship is expected to last three to six months, with a flexible start date. The pay range for this internship position will be 6-8K per month.

Mitsubishi Electric Research Laboratories (MERL), located in Cambridge, MA, is seeking a highly motivated and qualified individual to join our summer internship program and conduct cutting-edge research on hybrid AC and DC power grids. The ideal candidate is a senior or junior Ph.D. student in Electrical Engineering or a related field with strong expertise in HVDC power systems, power system analysis, power electronics and control, renewable energy integration, numerical methods and optimization. Proficiency in MATLAB, Python, or C/C++ programming is required. The internship is expected to last 3–4 months with a flexible start date, 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 seeking a highly motivated intern to collaborate in the development decision making, planning and control for teams of ground robot in task such as coverage control, monitoring and pursuit-evasion. 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 Fall/Winter 2025 (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 be6-8K 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.

The Computational Sensing team at MERL is seeking a highly motivated Ph.D. student for a research internship in machine learning for fluid dynamics, focusing on surrogate modeling of free-surface flows in engineered geometries. The goal of this project is to develop geometry-aware and physics-informed surrogate models for complex flow systems, combining high-fidelity simulations with modern neural architectures. 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 any of the following areas: operator learning, graph neural networks and geometric learning, particle-based methods, or differentiable simulation frameworks. Programming skills in Python and experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX 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.

MERL is seeking an intern to help develop a Python-OpenFOAM interface to enable active flow control through reinforcement learning. The ideal candidate would be a PhD student in engineering or related fields with a strong knowledge of OpenFOAM, C++, and Python. Experience with pybind11 or reinforcement learning are beneficial but not required. The intern will work closely with MERL researchers to develop the interface, conduct numerical experiments, and prepare results for publication. The duration is expected 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 MERL is seeking motivated and qualified individuals to develop algorithms that can perform background oriented schlieren (BOS) tomography. The project goal is to utilize both analytical and learning-based architectures to enable the reconstruction of 3D air flows in an indoor setting from BOS measurements coupled with physics informed machine learning. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, large-scale optimization, differentiable scene rendering, learning-based modeling for imaging, and physics informed neural networks. Preferred skills include experience with schlieren tomography, inverse rendering, neural scene representation, computational imaging hardware, and computationally efficient optimization of PINNs. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

Required Specific Experience

• Experience with differentiable/physics-based rendering.

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

The Computational Sensing team at MERL is seeking a highly motivated PhD student for an internship focused on uncertainty quantification (UQ) in computational modeling of physical systems. The goal of this project is to advance the methodology and practice of UQ, with a focus on generative models, reduced-order stochastic models, and optimal sensor placement for Bayesian inverse problems. The research will draw upon foundational ideas and techniques in applied mathematics and statistics for applications in wave propagation, fluid dynamics, and more generally high-dimensional systems. The ideal candidate will be a PhD student in engineering, applied mathematics, computer science, or related fields with a solid background and publication record in any of the following areas: generative models, stochastic modeling, dimensionality reduction, Bayesian inference, optimal experimental design, and tensor methods. Programming skills in Python or MATLAB 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.

MERL is seeking an intern to work on data-driven estimation and control for spatiotemporal dynamical systems, with applications in indoor airflow optimization. The ideal candidate would be a PhD student in engineering, computer science, or related fields with a strong background in estimation, control, and dynamical systems theory. Preferred skills include knowledge of reinforcement learning, reduced-order modeling (ROM) and partial differential equations (PDEs). The intern will work closely with MERL researchers to develop novel algorithms, conduct numerical experiments, and prepare results for publication. The duration is expected to be at least 3 months with a flexible start date.

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