The chair of Artificial Intelligence in Mechanics and Manufacturing at ETH Zurich, led by Prof. Dr. Dirk Mohr, combines physically motivated models with advanced ML approaches to solve challenges in constitutive and process modeling. We bridge the gap between theoretical research and industrial applications through close collaborations with industry partners, applying cutting-edge scientific discoveries to real-world manufacturing problems.

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. This project aims to overcome this limitation by integrating real-world casting data, process parameters, and finite element method (FEM) simulations using metamodeling techniques and Machine Learning (ML). By enriching datasets and leveraging advanced simulations to optimize ML models, we seek to enhance manufacturing efficiency and quality control in casting processes.

We are looking for a highly motivated researcher to join our team and contribute to projects at the intersection of computational modeling, data science, and industrial applications. As part of this position, you will:

• 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.
• Collaborate closely with industrial partners to translate research findings into practical applications.

We are seeking a candidate with the following qualifications:

• 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.
• A proactive, solution-oriented mindset with a willingness to engage in interdisciplinary research.
• Excellent communication skills and the ability to work effectively in a collaborative team environment.

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. You will:

• Conduct cutting-edge research in a dynamic and innovative team.
• Gain exposure to the Swiss manufacturing sector through collaborations with leading industrial partners.
• Contribute to real-world advancements in computational engineering and process optimization.

Join us and make a tangible impact on next-generation manufacturing technologies!

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. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish.

We look forward to receiving your online application with the following documents:

• cover letter
• CV
• transscirpts of all degress

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about our lab can be found on our website. Questions regarding the position should be directed to Martina Koch martikoc@ethz.ch .

For recruitment services the GTC of ETH Zurich apply.

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.