Join the cutting-edge next generation data storage technology development group located in our Bloomington, MN facility, and accelerate scientific discovery and engineering advances through deep neural networks. In this internship, the employee will use machine learning for the capture, interpolation, and optimization of highly complex optical phenomena in Heat-Assisted Magnetic Recording (HAMR), with emphases on development of new non-intuitive designs. To accomplish this, the employee will benefit from background in the fields of machine learning, photonics, plasmonic, and computational modeling. The employee’s prior experience with generative adversarial networks (GANs) and optical modeling, COMSOL software and/or equivalent programs, will contribute strongly to the success of these efforts.
About the role – you will:
- Gather datasets from COMSOL simulation of different plasmonic structures, pre-process them to make the input data structure feedable into the DNN model, separating proper training/validation sets.
- Research suitable DNN architecture (discriminative/generative approaches) that can be used to design plasmonic nano-antenna for HAMR application.
- Post-process, analyze, organize, and present results. Draw physically meaningful conclusions about configurations and expected performance and communicate findings.
- Suggest and model new configurations, materials, and modifications intended to improve the HAMR design.
About you:
- Experience with machine learning algorithms and optical simulations (COMSOL or other equivalent software).
- Experience with Linux and Windows computing environments.
- Experience with optimization methods is a plus.
- Introductory experience with programming languages (e.g. C++, Python, Java).
- Familiarity with MPI and high-performance computing.
- Good communication and teamwork skills are needed to collaborate with multiple organizations throughout the company.
Your experience includes:
- Pursuing a Ph.D. degree in Electrical Engineering, Physics, or related field and enrolled in Fall 2026 classes.
Location:
