Master in Artificial Intelligence and Cybersecurity

Interested in the future of technology? Then this jointly-run MSc programme might be the perfect fit for you! The universities of Klagenfurt and Udine collaborate to offer this highly focused program on the core subjects of Artificial Intelligence and Cybersecurity with an additional emphasis on the social, ethical and legal aspects that arise in practice.

The MSc in Artificial Intelligence and Cybersecurity is a two-year taught programme. It consists of three semesters of taught courses followed by a research project leading to the submission of a thesis and its defence at the end of the fourth term.

Over 50 students from all around the globe registered in the premiere of this MSc in October 2020 (all lectures were held online). The hands-on classes, the profound theoretical inputs as well as the multidisciplinary approaches were the major cornerstones making this first semester a huge success.

The application for the summer semester of 2021 is now open and students who are interested in this MSc will find more information here:

https://www.aau.at/en/studien/master-artificial-intelligence-and-cybersecurity/

Dynatrace announces award for the best Bachelor‘s and Master‘s thesis

Dynatrace is inviting students and graduates of the University of Klagenfurt to participate in the award for the best Bachelor’s and Master’s thesis in the IT field. The prizes will be awarded for the first time this year and will be announced annually from now on. Read more →

Master Thesis: “Predictive Analytics for Price and Demand Forecasting”

Modern business enterprises are facing complex market, resource and workforce management requirements, involving highly differentiated and dynamic processes, supply chains and demands. Artificial Intelligence (AI) technologies from the fields of Data Mining, Machine Learning and Recommender Systems are getting more and more pervasive to support strategic planning and decision making. The goal of this Master thesis is to perform a systematic investigation of major application areas and key AI technologies constituting the state of the art in predictive analytics for price and demand forecasting in energy, producing and service industries.

The Master thesis topic is suitable for students of Information Management or Applied Informatics. Depending on the specific focus the Master thesis takes, the supervision will be coordinated between:

  • Univ.-Prof. Dr. Martin Gebser
  • Univ.-Prof. Dipl.-Ing. Dr. Dietmar Jannach
  • Assoc.-Prof. Dipl.-Ing. Dr. Erich Christian Teppan
  • Postdoc-Ass. Dr. Christian Wankmüller

For further information, please contact Univ.-Prof. Dr. Martin Gebser (Martin [dot] Gebser [at] aau [dot] at), research group for Production Systems.

 

The following are some (incomprehensive) literature references, which can be consulted as a starting point for going more in depth or broadness while the Master thesis evolves:

  • P. Schwarenthorer, A. Taudes, J. Hunschofsky, C. Magnet, M. Tschandl: Increased Company Performance through Macroeconomics Sales Forecasting: A Case Study. Journal of Japanese Operations Management and Strategy 10(1): 1-17, 2020
  • M. Seyedan, F. Mafakheri: Predictive Big Data Analytics for Supply Chain Demand Forecasting: Methods, Applications, and Research Opportunities. Journal of Big Data 7: Article 53, 2020
  • B. Wu, L. Wang, S. Lv, Y. Zeng: Effective Crude Oil Price Forecasting using New Text-based and Big-Data-driven Model. Measurement 168: Article 108468, 2021
  • N. Ludwig, S. Feuerriegel, D. Neumann: Putting Big Data Analytics to Work: Feature Selection for Forecasting Electricity Prices using the LASSO and Random Forests. Journal of Decision Systems 24(1): 19-36, 2015

Master Thesis: “Goal Reasoning and Action Planning under Dynamics and Uncertainty”

Exogenous changes, sensing information and human-robot interaction turn plan generation and execution for autonomous intelligent agents into inherently dynamic and recurring tasks. First of all, multiple and sometimes conflicting goals need to be prioritized, where the success chances of plans for achieving the goals need to be taken into account. Moreover, plans may be based on sensing information, where the information acquisition and predictive evaluation of possible outcomes must be incorporated into the planning process. In multi-agent decision making, which particularly includes human-robot collaboration, reasoning about the capabilities, knowledge and goals of other agents is important to coordinate joint operations. Last but not least, real-world scenarios are subject to exogenous and often unpredictable changes in the environment; e.g., autonomous vehicles must constantly monitor the traffic to take safe actions.

In the light of these challenges, the goal of the Master thesis is to develop a demonstrator for dynamic goal reasoning and action planning in a selected application scenario from the robotics domain. The Master thesis will be co-supervised by members of the Department of Artificial Intelligence and Cybersecurity at the University of Klagenfurt and the JOANNEUM RESEARCH Robotics Institute at the Lakeside Science & Technology Park. This collaboration offers a unique opportunity to showcase Artificial Intelligence methods for planning and optimization in a practically relevant robotics environment, set up in simulation or even physically.

The following are some (incomprehensive) literature references, which can be consulted as a starting point for getting better intuition of the Master thesis topic and relevant research targets:

  • M. Rizwan, V. Patoglu, E. Erdem. Human Robot Collaborative Assembly Planning: An Answer Set Programming Approach. Theory and Practice of Logic Programming, 20(6): 1006-1020, 2020. https://arxiv.org/abs/2008.03496
  • B. Schäpers, T. Niemueller, G. Lakemeyer, M. Gebser, T. Schaub. ASP-Based Time-Bounded Planning for Logistics Robots. International Conference on Automated Planning and Scheduling, 2018. https://www.aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/download/17777/16944
  • P. Mazdin, M. Barcis, H. Hellwagner, B. Rinner: Distributed Task Assignment in Multi-Robot Systems based on Information Utility. International Conference on Automation Science and Engineering, 2020. https://ieeexplore.ieee.org/document/9216982
  • B. Reiterer, M. Hofbaur. Opportunistic Planning with Recovery for Robot Safety. German Conference on Artificial Intelligence, 2017. https://link.springer.com/chapter/10.1007/978-3-319-67190-1_31

The Master thesis topic is suitable for students of Applied Informatics, Artificial Intelligence and Cybersecurity, Information Technology or Information Management. For further information, please contact Univ.-Prof. Dr. Martin Gebser (Martin [dot] Gebser [at] aau [dot] at), research group for Production Systems.