Project Engineer: Data-Driven Optimization of Manufacturing Processes
The chair of Artificial Intelligence in Mechanics and Manufacturing at ETH Zurich combines physically motivated models with advanced Machine Learning approaches to solve challenges in constitutive and process modeling.
Numerical simulations are essential for designing and assessing the quality of casting products. However, their high computational cost presents a challenge for real-time process control.
About the Project
This project aims to integrate real-world casting data, process parameters, and finite element method (FEM) simulations using metamodeling techniques and Machine Learning (ML).
We seek to enhance manufacturing efficiency and quality control in casting processes by enriching datasets and leveraging advanced simulations to optimize ML models.
Your Role
* Develop and implement advanced Machine Learning models to analyze large datasets, including image and time-series data.
* Investigate and quantify the relationships between manufacturing process parameters, failure mechanisms, and product quality.
* Optimize manufacturing processes to enhance product reliability and quality.
Your Profile
To succeed in this role, you will need:
* A Master's degree in engineering, physics, computational sciences, or a related field.
* A strong interest in computational modeling and data-driven optimization.
* Proficiency in Python programming.
What We Offer
This position provides a unique opportunity to work at the intersection of research and industry, applying data-driven techniques to shape the future of manufacturing.
* Conduct cutting-edge research in a dynamic and innovative team.
* Gain exposure to the Swiss manufacturing sector through collaborations with leading industrial partners.
In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected.