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

Hello from the Cybersecurity research group!

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

Studienassistenz (7h/Woche) gesucht !!!

Die AAU Klagenfurt / TeWi – Abteilung Artificial Intelligence und Cybersecurity – sucht ab März 2021 eine Studienassistentin (7 h/Woche).

Aufgaben:

Umstellung der PPTX-Folien von Systemsicherheit auf LaTeX und Erweiterung und Pflege der SPU-Fragen für „Algorithmen und Datenstrukturen“, „Einführung in die theoretische Informatik“ und „Systemsicherheit“

Erwünscht sind:

– Deutschkenntnisse

– Genauigkeit

– Verlässlichkeit

– Organisationstalent und die Bereitschaft zur raschen Einarbeitung.

Formlose Bewerbungen an Peter Schartner erbeten.

Master’s Thesis: Development of a software environment for an online study in management

Topic

The master thesis will contribute to an experimental research study carried out at the Department of Management Control and Strategic Management. You will work on the platform for an interactive online experiment that involves human decision makers in a laboratory setting. The focus of the experimental study is in the field of task formation in a complex environment. 

Timing

As soon as possible upon individual agreement

Prerequisites

Strong programming skills

Supervision of the master thesis

Univ.-Prof. Dipl.-Ing. Dr. Dietmar Jannach
(dietmar [dot] jannach [at] aau [dot] at)

Department of Applied Informatics

Project team

Assoc. Prof. Dr. Alexandra Rausch
(alexandra [dot] rausch [at] aau [dot] at)

Assoc. Prof. Dr. Stephan Leitner
(stephan [dot] leitner [at] aau [dot] at)

Department of Management Control and Strategic Management