MERL is seeking a motivated and qualified individual to conduct research in dynamic modeling of vapor compression systems. Knowledge of data-based modeling techniques such as neural network and support vector regression 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. 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 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 motivated and qualified intern to conduct research on design optimization of electrical machines. The ideal candidate should have solid background and demonstrated research experience in mathematical optimization methods, especially in topology optimization, robust optimization, sensitivity analysis, and machine learning techniques. Hands-on experiences with the implementation of optimization algorithms, machine learning and deep learning methods are highly 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. Start date for this internship is flexible and the duration is about 3-6 months. 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 seeking a motivated and qualified individual to conduct research in modeling, simulation and control of aircraft electrical power system. The ideal candidate should have solid backgrounds in dynamic modeling and simulation of power electronics and electrical machine, and transient analysis of overall electrical power system. Demonstrated experience in physical modeling and simulation software/language such as Modelica or Simscape is a necessity. Knowledge of aircraft dynamics and aerodynamics is a big plus. Senior Ph.D. students in aerospace, electrical engineering, control are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

MERL is seeking a self-motivated intern to conduct fundamental research in the area of symbolic regression and deep learning for applications of recovering mathematical expressions or physical laws. The ideal candidate would be a senior PhD student with solid background in machine learning and strong publication record in top-tier venues. Prior experience in symbolic regression is strongly preferred. Very good Python, Pytorch/Tensorflow, and Matlab skills are required. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. The expected duration of the internship is 3 months and the start date 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 seeking a motivated and qualified individual to conduct research in design, modeling, and simulation of electrical machines. The ideal candidate should have solid backgrounds in modeling (including model reduction)/co-simulation of electromagnetics and thermal dynamics of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and real-time control experiments involving motor drives is a strong plus. Senior Ph.D. students in electrical or mechanical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3-6 months.