Doctoral (PhD) Student Positions in Control for Advanced Manufacturing

With our cutting-edge research, ETH Zürich's around 12’000 employees make essential contributions to the well-being of society for a future worth living.

In the Automatic Control Laboratory (IfA)  is a community of approximately 50 researchers 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.  The doctoral student positions we are looking to fill are 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. It involves complex physical and chemical processes 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. Our research aims to develop novel methods at the intersection of advanced control, optimization, manufacturing science, and machine learning, to create the next generation of sustainable automation solutions for modern manufacturing systems and supply chains.

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

1. Online learning-based control and control-oriented machine learning with application to advanced manufacturing systems, using methods such as Reinforcement Learning, Online Convex Optimization, and Iterative Learning Control.

2. System-level hierarchical controller optimization for industrial systems and decision-making using methods from bilevel, on-line, and differentiable optimization. 

3. Control and task planning for advanced manufacturing using domain knowledge and expert feedback using methods from domain adaptation, transfer learning, and large-language models and expert feedback in process planning. 

4. Digital twin-based learning and optimization for manufacturing processes such as 3D Printing, laser cutting, precision motion, robotic manipulation, using methods in machine learning, federated learning, and optimization.

In all cases, the results will be demonstrated on real-world advanced manufacturing and robotic systems in collaboration with industrial partners, helping to improve the efficiency and sustainability of their products.

Your goal will be to translate your own research ideas to tackle these challenges, in close collaboration with our interdisciplinary team. As part of this process, you will support our master students, publish in scientific journals, and participate in conferences. The positions are supported by the NCCR Automation and the European Project DMaaST that offer excellent opportunities for national and international collaboration with academic and industrial partners.

You are highly motivated and dedicated with a master’s degree in electrical, mechanical, or industrial engineering. Programming, modelling, and data analysis skills in python and machine learning/optimization libraries 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 August 2024 with a planned duration of 4 years.