Internship Openings

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.

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 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 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 seeks a motivated graduate student to develop scalable numerical solvers for systems composed of learned dynamical components. The internship will focus on the numerical challenges that arise when multiple neural-network-based component models are interconnected through physical constraints and feedback, forming large-scale coupled ODE/DAE systems. The intern will develop numerical methods to improve solver robustness and simulation speed for these large-scale systems. Candidates should have a strong foundation in numerical analysis and scientific computing, together with a solid background in machine learning and data-driven methods. Strong programming skills in Python, Julia, C/C++, or Matlab are expected. Senior PhD students in applied mathematics, scientific computing, computational engineering, or related fields are encouraged to apply. The internship is expected to last 3 months, with a flexible start date, and may be extended depending on research progress and mutual interest.

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

MERL seeks an intern for a three month assignment to conduct research into distributed estimation and control of interconnected dynamical systems. The topic is motivated by state estimation problems associated with distributed thermofluid systems that are governed by large scale, nonlinear but sparse differential algebraic equations. The modular architecture of these systems motivates a distributed estimation and control architecture. Qualified candidates should be enrolled in a Ph.D. or equivalent program of study and have knowledge of classic and advanced methods of multivariable and nonlinear control and estimation. Experience with networked control systems or multi-agent systems is an asset.

Required Specific Experience

• Experience with design and realization of classic and advanced nonlinear control systems.
• Experience with design and realization of state estimation algorithms.
• Knowledge of distributed estimation theory.
• Experience with networked control systems or multi-agent systems.

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