Postdoctoral researcher: Combining AI, adaptive control, and digital twins to optimize autonomo[...]

Are you passionate about cutting-edge automation technologies and their real-world applications? Join our team and the NCCR Automation consortium and take your research to the next level in our postdoctoral position, focusing on high-dimensional parameter optimization using digital twins for industrial processes.

School: School of Engineering
Starting date: January 2025 or by mutual agreement

Your role

Optimization of manufacturing and robotics systems is a central goal in the pursuit of sustainability and efficiency of advanced manufacturing systems. We plan to achieve it utilizing digital twins built with a dedicated available software/simulation platform, combined with modern data-driven optimization and control methods.
We are looking for motivated postdoctoral researcher to contribute to this effort. The envisioned research will address data-driven optimization of complex systems, using digital twins with different fidelities, and methods such as reinforcement learning, data-driven optimization and adaptive control. One big challenge is optimizing simultaneously multiple parameters, which requires developing novel, efficient computational methods and algorithms. The methods will be demonstrated on robotic systems available in our lab and at our industry partner’s premises (Siemens Digital Industries, Leuven, Belgium).
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 PhD, bachelor, and master students, publish in scientific journals, contribute to our teaching efforts, and participate in conferences. The position is supported by NCCR Automation and offers excellent opportunities for collaboration with academic and industrial partners. The position offers good opportunity for the development of coordination and project management skills, PhD student supervision, proposal applications, and leadership skills. The project duration is two years.
The position is hosted at the ZHAW Centre for AI, Industrial AI group, supervised by Dr. Alisa Rupenyan (Head of the Industrial AI group at ZHAW Centre for AI).

Your profile

You are highly motivated and dedicated with a doctoral degree in electrical, mechanical or industrial engineering, robotics, or in related topics in machine learning / AI, and a focus on optimization, data-driven methods for optimization (Bayesian optimization) and control. Background in control systems and robotics is highly beneficial. You are experienced as a researcher with an active interest in developing data-driven optimization-based solutions for autonomous (motion) systems to improve their efficiency and sustainability. Programming, modelling, and data analysis skills in Python will support you in contributing to our ongoing software development efforts. You are autonomous and interested in participating and managing collaboration projects, especially with the industry. We expect fluent English knowledge, and German could be beneficial.

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