Internship Openings

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.

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 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.

MERL is seeking a motivated and qualified intern to conduct research on machine learning based electric motor design and optimization. Ideal candidates should be Ph.D. students with a solid background and publication record in electric machine design, optimization, and machine learning. Hands-on experience with the implementation of optimization algorithms, machine learning and deep learning methods is required. Strong programming skills using Python/PyTorch are expected. Knowledge and experience with electric machine principle, design and finite-element analysis are highly desirable. Start date for this internship is flexible and the duration is about 3 months.

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.

Required Specific Experience

• Experience with [specific experience here].

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 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 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.

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.

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.