Courses we offer in winter term 2026/27.

Bachelor

• Algorithms and Data Structures 2 - study guide: VL 340300, UE (CS, Java) 34031x, UE (AI, Python) 34032x
  • 340.300, VO Algorithmen und Datenstrukturen 2, Alois Ferscha, 2026W
    • on campus at JKU Linz, distance participation via zoom, attendance not required
  • 340.310-3, UE Algorithmen und Datenstrukturen 2, Markus Jäger, 2026W
    • on campus at JKU Linz, Java, no distance participation, attendance not required
  • 340.320-33, UE Algorithmen und Datenstrukturen 2, M. Waleed Khan et al., 2026W
    • on campus at JKU Linz, Python, distance participation via zoom, attendance not required

• Practical Work in AI - study guide: PR 340912
  • 340.912, PR Practical Work in AI, Alois Ferscha et al., 2026W
    • on campus at JKU Linz, no distance participation, attendance required

Master

• Pervasive Computing:
  • Systems and Environments - study guide: VL 340500, UE 340510-513
  • Design and Development - study guide: VL 340600, UE 340610-613
  • 340.500, VL Pervasive Computing: Systems and Environments, Alois Ferscha, 2026W
    • on campus at JKU Linz, distance participation via zoom, attendance not required
  • 340.600, VL Pervasive Computing: Design and Development, Alois Ferscha, 2026W
    • on campus at JKU Linz, distance participation via zoom, attendance not required
  • 340.510-13/610-13, UE Pervasive Computing: Systems and Environments / Design and Development, Denzler/ Tretiak, 2026W
    • on campus at JKU Linz, no distance participation, attendance required, both courses should be done in the same term.

• Seminar and Practical Work in AI (Master)
  • Seminar in AI (Master) - study guide: 340940 
  • Practical Work in AI (Master) - study guide: 340930 
  • 340.930/40, PR / SE in AI (Master), Alois Ferscha, 2026W
    • on campus at JKU Linz, no distance participation, attendance required, both courses should be done in the same term.

• Master's Thesis Seminar
  • Master's Thesis Seminar WS (for CS) - study guide: SE 340020
  • Master's Thesis Seminar (for AI) - study guide: SE 340920