- Electric Motor Design Optimization

ZipRecruiter; Cambridge; On-site; GBP 25,000 - 35,000; Full time

Job summary

A leading research organization is seeking a motivated Ph.D. student to conduct innovative research on electric motor design optimization using physics-informed neural networks. The ideal candidate will have a strong background in electromagnetic modeling and machine learning. This role offers a flexible start date and a duration of 3-6 months, providing an excellent opportunity to contribute to cutting-edge research.

Qualifications

• Experience with modeling and simulations for motor design.

Responsibilities

• Conduct research on physics-informed neural network-based modeling for electric motor design optimization.
• Work on 2D/3D electromagnetic modeling and simulation.

Skills

Machine learning

Coding

Education

Ph.D.

Tools

ANSYS

Python

Job description

MERL is seeking a motivated and qualified individual to conduct research on physics-informed neural network-based modeling for electric motor design optimization. Ideal candidates should be Ph.D. students with a solid background and proven publication record in one or more of the following research areas:

1. 2D/3D electromagnetic modeling and simulation
2. Analytical modeling methods for electromagnetics and iron losses (e.g., magnetic equivalent circuit)
3. Machine learning-based surrogate modeling

Strong coding skills with ANSYS or open-source FEM software and Python-based learning libraries are required. Prior experience with running jobs over clusters is a plus. The start date is flexible, and the duration is 3-6 months.

Required Specific Experience

• Experience with modeling and simulations for motor design

Note: MERL provides equal employment opportunities to all employees and applicants without regard to race, color, religion, sex, national origin, age, disability, or genetic information. MERL complies with applicable laws governing nondiscrimination and prohibits workplace harassment based on these attributes. Employment is contingent upon full authorization to work in the U.S. and compliance with export control regulations, which may affect employment start date and access to certain information and technology.