As Austria's largest research and technology organisation for applied research, we are dedicated to make substantial contributions to solving the major challenges of our time, climate change and digitalisation. To achieve our goals, we rely on our specific research, development and technology competencies, which are the basis of our commitment to excellence in all areas. With our open culture of innovation and our motivated, international teams, we are working to position AIT as Austria's leading research institution at the highest international level and to make a positive contribution to the economy and society.

Our Center for Vision, Automation & Control located inVienna invites applications for a master’s thesis. The Center for Vision, Automation & Control exploits the opportunities provided by automation and digitalisation to initiate and advance innovation for industry, primarily in Austria and Europe. Our main goal is to take away the monotonous, heavy, difficult and dangerous aspects of people’s work through innovation. We help our partners by making technical systems more robust, reliable, flexible and easy to use. At the same time we increase the resource efficiency of industrial processes by reducing waste, product failures and emissions.

For this master’s thesis you will join our Competence Unit Complex Dynamical Systems , which focuses on the development and deployment of algorithms to control various types of systems - starting from low-energy applications like electronics and drive systems, to (large-scale) robotics and heavy industrial applications. We are working in projects in cooperation with national and international research organisations and companies leading their market sector.

• The focus of this master’s thesis is the investigation of meta learning techniques for fast model adaptation in low-data scenarios. This is especially relevant for reducing end of line commission times for high-mix low-volume systems .
• You will support us in the field of data-driven modeling and identification of nonlinear dynamical systems .
• You will familiarise yourself with state-of-the-art approaches in transfer learning and meta learning for system identification through a structured literature review.
• Under the guidance of our researchers, you will re-implement and evaluate selected reference methods from current research (e.g. in Python or MATLAB) to build a solid methodological foundation.

• With the support of our team, you will design and develop a simulation-based validation environment to assess the performance of the implemented methods.
• You will analyse and compare the adaptability and efficiency of different approaches on nonlinear system identification tasks.
• Optionally, you will apply and validate your methods on a real-world valve test bench to demonstrate practical applicability.
• You may publish your results in a scientific journal and present them at a conference.