About the job
As industries globally strive for excellence in manufacturing and optimization, the demand for advanced design and simulation techniques is more crucial than ever. In a competitive market landscape, traditional workflows face challenges in managing the complexities of interrelated parameters impacting friction and wear. The high computational demands of precise multiphysics simulations in contact dynamics and elastohydrodynamic lubrication (EHL) often impede their practical application in design processes. Currently, a robust, experimentally validated framework to systematically tackle these tribological design challenges is lacking.
- Your primary responsibility will involve pioneering the scientific groundwork for a machine learning-driven multiphysics framework utilizing surrogate models calibrated with validated EHL simulations.
- You will also be tasked with developing a groundbreaking, computationally efficient, data-centric design protocol tailored for lubricated components.
- This role will significantly expedite the design cycle for intricate EHL challenges, facilitating the creation of more durable, efficient, and reliable tribological components for essential industrial applications.
- At the forefront of merging AI with classical engineering design, you will play a pivotal role in innovation.
- Ultimately, you will acquire expertise in leveraging machine learning to solve complex engineering problems, positioning yourself as a valuable asset for future leadership roles in both industry and academia.