MERL is looking for a highly motivated and qualified candidate to work on estimation and model structure identification for digital twins of multi-physical systems. The research will involve study and development of white-box and grey-box model calibration and identification methods suitable for large-scale systems. The ideal candidate will have a strong background in one or multiple of the following topics: nonlinear estimation, model identification, optimization, data-driven and reduced order modeling, and machine learning; with expertise demonstrated via, e.g., peer-reviewed publications. Prior programming experience in Julia/Modelica is a plus. Senior PhD students in mechanical, electrical, chemical engineering or related fields are encouraged to apply. The typical duration of internship is 3 months and start date is flexible.

MERL is looking for a highly motivated and qualified candidate to work on modeling for smart sustainable buildings. The ideal candidate will have a strong understanding of modeling renewable energy sources, grid-interactive buildings, occupant behavior, and dynamical systems with expertise demonstrated via, e.g., peer-reviewed publications. Hands-on programming experience with Modelica is preferred. The minimum duration of the internship is 12 weeks; start time is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

MERL is looking for a highly motivated and qualified candidate to work on Bayesian Optimization and predictive control for net-zero energy buildings. The ideal candidate will have a strong understanding of control, optimization, and/or machine learning with expertise demonstrated via, e.g., publications, in at least one of: Bayesian optimization, (stochastic) model predictive control, reinforcement learning, controller tuning; additional understanding of energy systems is a plus. Hands-on programming experience with numerical optimization solvers and Python is preferred. PhD students are strongly preferred, as an expected outcome of the internship is a publication in a high-tier venue. The minimum duration of the internship is 12 weeks; start time is flexible.

MERL is seeking a highly motivated and qualified individual to conduct research in dynamic modeling and simulation of vapor compression systems in the summer of 2023. Knowledge of data-driven modeling techniques is required. Experience in working with thermo-fluid systems is preferred. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. Senior Ph.D. students in applied mathematics, chemical/mechanical engineering and other related areas are encouraged to apply. The expected duration of the internship is 3 months and the start date is flexible.

MERL is seeking a motivated graduate student to research numerical methods pertaining to the simulation, control, and optimization of large-scale systems. 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 numerical methods, control, 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 intern for a research position in spacecraft attitude dynamics and control. The ideal candidate is a PhD student with experience in attitude kinematics and dynamics, multi-body dynamics, Lagrangian or Hamiltonian mechanics, optimization, and control of rigid bodies. Experience in computational fluid dynamics (CFD) using OpenFOAM, multi-phase flow modeling, and volume-of-fluid approach is desirable. Publication of results produced during the internship is expected. The duration of the internship is 3-6 months, and the start date is flexible.

MERL is looking for a self-motivated intern to work on monitoring of motor drive system via vibration analysis. The ideal candidate would be a Ph.D. candidate in electrical engineering or mechanical engineering with solid research background in signal processing, vibration analysis, and mechanical system. Proficiency in Matlab or other related software is necessary. Experience in machine learning is a plus. The intern is expected to collaborate with MERL researchers to perform model simulation, data analysis, and prepare manuscripts for scientific publications. The total duration is anticipated to be 3 months and the start date is flexible. This internship requires work that can only be done at MERL.

MERL is seeking a motivated and qualified individual to conduct research in modeling, control, simulation and analysis of electric system involved in xEV and electric aircraft. The ideal candidate should have solid backgrounds in some of the following areas: modeling, control, and simulation of electrical systems (including generators, motors, power electronics and batteries), aerodynamics, mission analysis, flight dynamics, and multi-disciplinary system design optimization. Demonstrated experience in software/language such as Modelica or Matlab/Simulink/Simscape is a necessity. Knowledge and experience of CarSim, NPSS, SUAVE, and FLOPS is a definite plus. Senior Ph.D. students in automotive, aerospace, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

MERL is seeking a motivated and qualified intern to conduct research on topology optimization of electrical machines. The ideal candidate should have solid background and demonstrated research experience in mathematical optimization methods, in particular in topology optimization, robust optimization, and sensitivity analysis. Hands-on coding experiences with the implementation of topology optimization algorithms and finite-element simulation are desirable. Knowledge and experience with electric machine principle, design and finite-element analysis is a strong plus. Senior Ph.D. students in related expertise are encouraged to apply. The start date is flexible and typical duration is about 3 months.