Internship Openings

5 / 19 Intern positions were found.

Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

MERL expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of MERL's employees to perform their job duties may result in discipline up to and including discharge.

Working at MERL requires full authorization to work in the U.S and access to technology, software and other information that is subject to governmental access control restrictions, due to export controls. Employment is conditioned on continued full authorization to work in the U.S and the availability of government authorization for the release of these items, which might include without limitation, obtaining an export license or other documentation. MERL may delay commencement of employment, rescind an offer of employment, terminate employment, and/or modify job responsibilities, compensation, benefits, and/or access to MERL facilities and information systems, as MERL deems appropriate, to ensure practical compliance with applicable employment law and government access control restrictions.

In addition to base pay, interns receive a relocation stipend, covered travel to and from MERL, and a monthly Charlie Card for local commuting. Interns are invited to participate in weekly social gatherings and professional development opportunities, including research talks by both internal and external speakers. Interns who meet the 90-day waiting period are also eligible for health insurance coverage. MERL provides immigration support for qualified candidates as needed. Employment is considered "at-will," and the Company reserves the right to modify base salary or any other compensation program at any time, including for reasons related to individual performance, departmental or Company performance, and market conditions.


  • OR0330: Internship - EMT Simulation of Data-Center Power Systems

    • 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.

    • Research Areas: Electric Systems, Optimization, Machine Learning, Dynamical Systems, Control, Artificial Intelligence
    • Host: Hongbo Sun
    • Apply Now

  • MS0254: Internship - Decentralized Data Assimilation for Large Scale Systems

    • 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.

    • Research Areas: Machine Learning, Multi-Physical Modeling, Dynamical Systems, Control, Optimization
    • Host: Vedang Deshpande
    • Apply Now

  • MS0259: Internship - Multi-Fidelity Dynamic Models for Energy Systems

    • 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.

    • Research Areas: Multi-Physical Modeling, Dynamical Systems, Optimization, Data Analytics
    • Host: Hongtao Qiao
    • Apply Now

  • MS0326: Internship - Scalable Numerical Solvers for Systems of Learned Dynamical Components

    • 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.

    • Research Areas: Dynamical Systems, Machine Learning, Multi-Physical Modeling
    • Host: Hongtao Qiao
    • Apply Now

  • MS0337: Internship - Distributed estimation for interconnected dynamical systems

    • 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.

    • Research Areas: Control, Dynamical Systems, Multi-Physical Modeling
    • Host: Scott Bortoff
    • Apply Now