Master Thesis "Meta Learning in Nonlineary Dynamic System Modelling"

Master Thesis "Meta Learning in Nonlineary Dynamic System Modelling"

CENTER FOR VISION, AUTOMATION & CONTROL

AIT Austrian Institute of Technology, Austria's largest research and technology organisation for applied research, invites applications for a master's thesis at the Center for Vision, Automation & Control in Vienna.

The Competence Unit Complex Dynamical Systems focuses on the development and deployment of algorithms to control various types of systems, from low-energy applications like electronics and drive systems to robotics and heavy industrial applications.

Tasks

• Investigate meta learning techniques for fast model adaptation in low-data scenarios.
• Support data-driven modeling and identification of nonlinear dynamical systems.
• Conduct a structured literature review on transfer learning and meta learning for system identification.
• Re-implement and evaluate selected reference methods from current research, for example in Python or MATLAB.
• Design and develop a simulation-based validation environment.
• Analyse and compare the adaptability and efficiency of different approaches on nonlinear system identification tasks.
• Optionally validate methods on a real-world valve test bench and publish results in a scientific journal or conference.

Requirements

• Ongoing master's studies in electronics, technical mathematics, technical informatics, data science or a comparable technical field.
• Enjoyment of application-oriented industrial questions.
• Good knowledge of Python or MATLAB.
• Good knowledge of machine learning.
• High level of commitment and team spirit.
• Very good English or German skills, spoken and written.

Benefits

• Duration of the master's thesis project: 6 months.
• Start date as soon as possible, with flexibility depending on availability.
• EUR 616,44 gross per month for 12 hours per week based on the collective agreement, plus additional company benefits.
• Supportive research environment with extensive experience in supervising master's theses.
• Insights into interdisciplinary research at the intersection of machine learning, control engineering and system identification.

For further information please contact DI Markus Gurtner, markus.gurtner(at)ait.ac.at.

Please submit your application documents online via Apply now.