Seagate Research Group (SRG) needs to perform experiments and simulations to guide the design of future hard-disk drive based, and other, advanced data storage and memory technologies. This work directly informs the Company’s product roadmaps. Modeling and simulation are used to evaluate designs up to 10 years in the future, as well as elucidate experimentally observed phenomena. Machine learning models are also being developed to predict product reliability and inform future materials and design directions.
Requirements
- Background experience in numerically solving partial differential equations using finite difference and/or element methods.
- Programming experience in Python
- Good understanding and experience in machine learning and deep learning, e.g., DNN, CNN, Transformer, Reinforcement Learning, Active Learning, GAN, etc.
- Some familiarity with DeepONet, FNO, and/or other techniques using machine learning for surrogate modeling.
Responsibilities
- Develop AI based surrogate models for fast design simulation of physics-based models.
- Create novel validation techniques to aid in improving confidence in AI tools.
- Explore the literature to propose novel machine learning architectures and processes.
- Develop state-of-the-art AI/ML and data science solutions to solve/optimize technical problems in advanced technology areas
Other
- Passionate about data science and physics modeling.
- Developer that desires their tools to be used in practice with high confidence.
- Self-motivated, independent, and a team player with strong communication and interpersonal skills
- Innovative with a growth mindset, willing to learn new concepts and comfortable working outside your comfort zone
- Must be enrolled in Fall 2026 classes at a US university.
