Postdoctoral Researcher in AI-driven Surrogate Modeling for Manufacturing Process Optimization

Postdoctoral Researcher in AI-driven Surrogate Modeling for Manufacturing Process Optimization
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Are you passionate about applying artificial intelligence to revolutionize traditional manufacturing processes? We are looking for a talented and motivated postdoctoral researcher to join our multidisciplinary team, working at the intersection of AI and mechanical engineering. In this exciting role, you will contribute to the development of innovative AI-based surrogate models that significantly accelerate forging process simulations, optimize manufacturing parameters, and reduce dependency on computationally expensive simulations.

This position offers the opportunity to work with leading experts, access state-of-the-art computational resources, and contribute to high-impact research that bridges academia and industry. If you have a strong background in AI, machine learning, and a keen interest in real-world applications, we encourage you to apply and be part of this exciting journey.

Project background
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Manufacturing process optimization is a critical challenge in the modern industrial landscape, particularly in high-precision industries such as aerospace, automotive, and energy. Current methodologies in processes like forging rely heavily on computationally expensive simulations based on Smoothed Particle Hydrodynamics (SPH) or Finite Element Method (FEM), which require significant computational resources and expert knowledge. These traditional methods lead to prolonged development cycles and increased costs. The demand for rapid and accurate process optimization solutions is increasing, driven by the need to reduce time-to-market and production costs while maintaining high-quality standards.

This project aims to develop a novel AI-driven solution to revolutionize forging process optimization by leveraging surrogate modeling techniques. The objective is to create an AI-based tool that can:

Accurately simulate forging processes in milliseconds instead of hours
Predict optimal process parameters with high accuracy
Reduce dependency on expensive simulations based SPH or FEM
Enable real-time decision-making in production environments

Job description
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Design and implement data augmentation techniques to enhance model performance
Implement efficient parametrization methods for geometries and workpiece properties such as temperature or strain
Develop and optimize AI-based surrogate models for manufacturing processes
Develop a user-friendly graphical interface (GUI) for interaction with the AI model, including real-time analytics and visualization
Prepare manuscripts, reports, and presentations to disseminate findings
Maintain thorough documentation of model development and project progress

Profile
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Required

PhD in Computer Science with the focus on AI
Proficiency in python programming 
Strong expertise in machine learning and deep learning frameworks (especially PyTorch)
Demonstrated ability to work independently and as part of a multidisciplinary team
Excellent written and verbal communication skills (Proficiency in English; German is a plus)

Preferred

Basic familiarity with FEM and SPH
Knowledge of manufacturing processes, especially forging
Experience with computer graphics concepts such as implicit modeling, mesh processing, or CAD-related tasks
Familiarity with reinforcement learning

We offer
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The open position is in the Advanced Manufacturing Laboratory (Prof. Dr. Markus Bambach) within the Department of Mechanical and Process Engineering (D-MAVT) at ETH Zurich in Zurich, Switzerland.

We offer a 2-year project-based contract starting in Spring/Summer 2025 that includes:

Opportunities to engage with different communities bridging machine learning and mechanical engineering leading to high impact publications
You will be part of a highly motivated, multidisciplinary and collaborative team
We will support your scientific career and application for postdoctoral fellowships on your path towards scientific leadership
Access to state-of-the-art computational resources and collaborative research networks
Opportunities for professional development and career advancement
Working, teaching and research at ETH Zurich (https://ethz.ch/en/the-eth-zurich/working-teaching-and-research.html)

We value diversity
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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.

Curious? So are we.
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We look forward to receiving your online application with the following documents:

a letter of motivation (1-page max)
CV 
PhD diploma

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 the website. Questions regarding the position should be directed to Dr. Jan Petrik, by email at [email protected] (no applications).

About ETH Zürich
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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.