PhD Students in Cybersecurity wanted!

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The Cybersecurity Research Group – hosted at the university’s Digital Age Research Center (D!ARC) – is seeking to fill the post of a Phd-student within the area of Side Channel Resistent Embedded Systems.

The Cybersecurity Research group has been established with a clear interest in finding and eliminating information leaks in the context of embedded devices (they often exhibit a number of so called side channels). In this particular setting, the research team – led by Prof. Elisabeth Oswald – has developed a range of techniques that integrate well in a typical software flow and make leakage information transparent to a developer.

Currently, the group consists of one further lecturer, three postdocs, and two further PhD students who work in the areas of cryptography, statistical machine learning, embedded security, artificial intelligence and deep learning as well as crypto engineering.

The purpose of this studentship is to build on the existing work and add novel ideas including the automated tracing of leaks from lower-level code representations to the description in a higher-level language, the development of code transformation techniques to mitigate leaks automatically, etc. The successful applicant will work closely with Prof. Oswald and will develop into a researcher/engineer with a profound understanding of the challenges of leakage resilient development.

Requirements:

✓ Master’s level qualification in informatics, mathematics or other technical sciences
✓ strong background in embedded systems
✓ some background in low level programming and embedded systems
✓ Very good language skills in English (German optional)
✓ Willingness to work within an international team

The vacancy shall be filled as soon as possible.
If you are interested in this opportunity please consider applying with your CV and a motivational letter to elisabeth [dot] oswald [at] aau [dot] at

Offloading computation to 5G networks: Helping drones to improve their autonomous navigation

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Commercial drones usually come equipped with modest on-board computing power. Consequently, their speed and agility are somewhat limited when they use their cameras like eyes to navigate in space. Samira Hayat, a researcher at the Department of Information Technology, recently joined forces with colleagues from other departments and Deutsche Telekom to investigate the effects of offloading computation to the edge of the network (edge computing).

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Game Studies and Engineering: It’s the passion that drives me

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After achieving a bachelor’s degree in Computer Science and Engineering at IIIT Una, India, Shivi Vats decided to come to Klagenfurt to continue studying with the master’s programme Games Studies and Engineering. In this interview he talks about why one should study at AAU and what advantages Klagenfurt has over a big city like Delhi, India.

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“The University of Klagenfurt is an excellent university. The teaching and research activities in the field of computer science are superb.”

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An interview with Thomas Grassauer about the cooperation between Dynatrace Austria GmbH and the University of Klagenfurt.
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Master in Artificial Intelligence and Cybersecurity

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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

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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”

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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”

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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.

Barbara Pedretscher: “Use the university as your network of knowledge and exchange!”

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Our graduate Barbara Pedretscher studied Technical Mathematics. She completed her compulsory internship at KAI Kompetenzzentrum Automobil- und Industrieelektronik GmbH and was subsequently invited to write her diploma thesis and doctoral dissertation there as well. Today she works in the field of R&D Reliability and Data Science. We chatted with her about the many possibilities afforded by a university education and about spending memorable years at university. Read more →

Hello from the Cybersecurity research group!

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Established within the university’s Digital Age Research Center (D!ARC) the Cybersecurity research group goes into it’s second year of activities. The group’s research areas are based within cryptography, statistical machine learning, embedded security, artificial intelligence and deep learning as well as crypto engineering.

The group’s main expertise is in the area of deployment aspects of cryptography. Such aspects are related to information leakage (via side channels), and the detection and prevention of such channels; practical cipher constructions for specific application areas; secure implementation techniques and tools; and the application of machine learning and deep learning in the context of cybersecurity.

The Cybersecurity research group is currently running an ERC funded Consolidator Grant project, titled SEAL („Sound and Early Assessment of Leakage for Embedded Software“). It tackles the challenge to developed tools that are sophisticated enough to predict a range of side channel leakage behaviours for modern processors.

In the last months the team has grown to the number of ten. Currently the group consists of one professor, one lecturer, three postdocs, three PhD students, one technician and one administrator. The group represents five different countries, making work and communication a truly international and cultural experience.

You will find more information here:
www.aau.at/digital-age-research-center/cybersecurity/

And here:
www.cybersecurityresearch.at/

If you are interested to explore collaborations in any shape or form, let’s talk!
Please email to Elisabeth [dot] Oswald [at] aau [dot] at