Doctoral (PhD) Student Positions in Control and Optimization for 3D Printing

The Automatic Control Laboratory (IfA) in the Department of Information Technology and Electrical Engineering of ETH Zurich is a community of approximately 50 researchers from more than 20 countries working on the development of methods and computational tools for automation, exploring their potential for promoting our social well-being in areas such as energy systems, transportation, and industrial processes. We are looking for two doctoral students to join our international team and contribute to our research efforts in the area of control and automation of industrial processes. They are supervised by Prof. John Lygeros in collaboration with Dr. Efe Balta from Inspire AG and Prof. Alisa Rupenyan from the Zurich University of Applied Science (ZHAW).

Advanced manufacturing is central to sustainable automation with high-impact opportunities in both research and society. Additive manufacturing, or 3D printing, processes offer unprecedented flexibility and productivity to modern manufacturing systems. Additive manufacturing processes involve complex physical and chemical interactions that need to be executed with high precision and minimal interruptions throughout a life cycle. Incorporating predictive models and advanced control using data opens up exciting new possibilities in this domain. The research activities within these projects will bring forward the integration of novel methods at the intersection of advanced control, optimization, manufacturing science, robotics, and machine learning. The two doctoral student positions we are looking to fill aim to explore the interplay between these areas. They will be integrated in the wider team of the Automatic Control Laboratory and our efforts to build the next generation 3D printing systems.

We are looking for two motivated doctoral students to contribute to this effort. The envisioned research will address:

1. Extrusion process control and digital twin development for extrusion 3D printing: 

• Development and improvement of novel in-situ sensing techniques to be used in advanced process control methods. 
• Learning-based and adaptive control methods for extrusion process control and precision printing. 
• Digital twin-driven methods for online process monitoring, process validation, and prediction using modern AI tools.

1. 3D Printing process planning, optimization, and control: 

• Process optimization and optimal path planning for polymer extrusion 3D printing. 
• Hierarchical control structures for 3D printing processes studying process control and motion control problems. 
• Integration of modern AI tools such as reinforcement learning and online learning-based control, combined with process digital twins. 
• Co-design of control architectures for cyber-physical systems.

In all cases, we plan to demonstrate the results on custom-built real-world 3D printing setups available at IfA and inspire. For a large scale proof of concept, we will implement the results on a testbed combining robotics and 3D printing currently developed at ZHAW, in collaboration with industry partners.

You are highly motivated and dedicated with a Master’s degree in electrical, mechanical, or industrial engineering, or related field. Programming, modelling, and data analysis skills in python and machine learning/optimization libraries/toolboxes support you in contributing to our ongoing software development efforts. Your spoken and written English skills help you navigate our international environment.

We are offering a multifaceted and challenging position in a modern research environment with excellent infrastructure. The ideal starting date is July 2025 with a planned duration of 4 years.