Seagate Research Group (SRG) is looking for a candidate to develop AI based surrogate models for fast design simulation of physics-based models to guide the design of future hard-disk drive based, and other, advanced data storage and memory technologies. The work performed by this team directly informs the Company’s product roadmaps.
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
- Pursuing a PhD degree in Materials Science, Chemistry, Physics, Computer Engineering, Electrical engineering, Computer Science, Data Science, Math, or other related areas. Must be enrolled in Fall 2026 classes at a US university.
