Internship Openings

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 an intern to work on physics-informed scientific machine learning algorithms for problems governed by partial differential equations (PDEs). The ideal candidate would be a PhD student in engineering, computer science, or related fields with a strong background in scientific machine learning for PDEs. Preferred skills include experience with autoencoders, transformers, or diffusion models. Strong coding abilities in Python and a deep learning framework such as Pytorch are essential. The intern will work closely with MERL researchers to develop novel algorithms, conduct numerical experiments, and prepare results for publication. The duration is expected to be at least 3 months with a flexible start date.

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

MERL is seeking an intern to help develop a Python-OpenFOAM interface to enable active flow control through reinforcement learning. The ideal candidate would be a PhD student in engineering or related fields with a strong knowledge of OpenFOAM, C++, and Python. Experience with pybind11 or reinforcement learning are beneficial but not required. The intern will work closely with MERL researchers to develop the interface, conduct numerical experiments, and prepare results for publication. The duration is expected to be at least 3 months with a flexible start date.

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

MERL is looking for a self-motivated intern to research on problems related to complex physical reasoning using digital twins and large vision-and-language models (VLMs). An ideal intern would be a Ph.D. student with a strong background in computer vision, machine learning, and robotics, with broad experience in using state-of-the-art physics engines. The candidate must have a strong background in 3D computer vision and machine learning (specifically in robotics and reinforcement learning), operational knowledge in using VLMs and generative AI, and experience in solving physical reasoning problems. Prior experience training VLMs would be a strong plus. The intern is expected to collaborate with researchers from multiple teams at MERL to develop algorithms and prepare manuscripts for scientific publications.

Required Specific Experience

• Experience with state-of-the-art physics simulators (both differentiable and non-differentiable)
• Experience in neuro-physical reasoning approaches
• Experience in state-of-the-art large vision-and-language models and generative AI models
• Enrolled in a PhD program
• Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

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 condition monitoring and fault diagnosis. The intern will contribute to the development of advanced monitoring and diagnostic technologies, with applications that may include electric motors and motor-driven systems. Ideal candidates should be Ph.D. students with a solid background and publication record in one or more of the following research areas: fault diagnosis, prognosis, and health management; electric machine modeling and data analysis; machine learning techniques including transfer learning and domain adaptation for fault diagnosis. Strong programming skills in Python and familiarity with frameworks such as PyTorch are required. Experience with modeling and analysis of electric machines is highly desirable. Senior Ph.D. students in related fields (e.g., Electrical Engineering, Mechanical Engineering, Applied Physics) are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.

The pay range for this internship position will be 6-8K 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 motivated and qualified intern to conduct research on shape and topology optimization. The intern will contribute to the development of topology optimization algorithms for engineering design problems, with applications that may include electromagnetic devices and mechanical structures. Ideal candidates should have a solid background and demonstrated research experience in mathematical optimization methods, including topology optimization, robust optimization, sensitivity analysis, and machine learning-based optimization. Hands-on coding experience in implementing topology optimization algorithms and performing 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 fields (e.g., Electrical Engineering, Mechanical Engineering, Applied Physics) are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.

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