The Institute for Automation at FHNW and the Automatic Control Laboratory at ETH Zurich jointly offer a PhD position based in Brugg, Switzerland, within NCCR Automation for the academic session 2021-2022.The project is funded by the Swiss National Science Foundation.
The position will be supervised by Prof. Dr John Lygeros and Dr Alisa Rupenyan at ETH Zurich, and Prof. Dr Jürg Keller at FHNW Brugg-Windisch.
|Scholarship Sponsor||ETH Zurich|
|Scholarships level||PhD Position|
|Award Amount||Not Specified|
|Fellowship Period||Not Specified|
|Study area||Computer Science, Microtechnics|
|Opening date||March 02, 2021|
Research in robotics has seen incredible progress in the last years, driven by the incorporation of sensor data in the control algorithms of the robotic systems. Learning has enabled self-driving, autonomous flight, and humanoid robots to reach previously incredible milestones.
Such exciting development is much slower adopted in modern manufacturing processes. The current state of manufacturing technology is mature enough to accommodate and benefit from data-driven, learning-based approaches.
Incorporating efficient use of predictive models and advanced control using process data opens up new possibilities for emerging manufacturing processes. The research activities within these projects will bring forward the integration of advanced control methods combined with machine learning.
We work towards creating a data-informed automation framework demonstrated on high-end industrial systems, unifying ideas from black-box adaptive control, iterative learning, and predictive control.
The approach consists of three pillars:
- Scalability: ensuring that multiple parameters can be optimized simultaneously
- Safety: ensuring that each combination of parameters is within the safe operational bounds of the motion system
- Efficiency: providing that optimal parameters are found with a minimal number of iterations
The research activities in this interdisciplinary field comprise several of the following activities:
- Use and comprehension of a system/process model using sensor data and plant parameters
- Adaptation or development of an appropriate control algorithm, applying an optimization-based approach
- Demonstration of the system
It is part of the NCCR Automation, which means close interaction with 16 control groups and 42 PhDs and postdocs across Switzerland on this project. The employment will be with the FHNW.
The applications are open now
Suitable candidates need a graduate degree in engineering or a related field from a recognized university. Strong background in data modelling, programming (MATLAB, Python, C++), and proven understanding of control methods are essential for completing the proposed research.
We look forward to receiving your application, including a CV, 2-3 reference letters/contacts, a short statement of research interests and objectives, one publication/thesis, and transcripts of all degrees in English.
Please submit your application via email to Dr Alisa Rupenyan, email [email protected] and Prof. Jürg Keller [email protected]