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 digitisation. 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 in Vienna invites applications for a Master Thesis. At the Center for Vision, Automation & Control our research unit " Complex Dynamical Systems ”focuses its research on modelling, simulation, analysis and optimisation, as well as on the control of complex and dynamical industrial systems and processes. The primary goal is to enhance product quality, increase flexibility, and boost resource efficiency. Applications are found in the fields of mechatronics, robotics, and industrial production facilities.

The Master Thesis is part of the lighthouse project AI Enabled Sustainable Automation, where new methods and technologies are developed to enable zero-defect manufacturing in high-pressure die-casting. In die casting, molten metal is transported by a piston into the mould, while a temperature control unit continuously controls its temperature. In the tempered mould, the solidification of the metal occurs. All these processes have an impact on the quality of the die-cast part and the efficiency of the die-casting process. In this Master Thesis, we are conducting research on modelling, monitoring and optimising the quality of die-cast parts with the aim of reducing scrap and increasing resource efficiency.

• The main goal of this master’s thesis is to develop a strategy for process monitoring and optimisation (optional).
• Under the guidance of your interdisciplinary team, you will carry out a literature search, develop data-driven models for quality features depending on machine settings and variables and a model-based strategy for process monitoring and optimisation.
• You will gain knowledge in quality control and analyse multimodal data (e.g. rational data, timeseries data, images).

• You will implement the mathematical models and the monitoring and optimisation algorithms in Python / MATLAB.
• You will accumulate experience in a research field, that deals with the triple transition.
• You will gain experience working alongside an international team of engineers and scientists while expanding your professional network.