GPU4U Pilot Project (Student-focused GPU access within DHInfra.at)

The GPU4U project initiated by AICS and ITEC (PI: J. Wachter & Mathias Lux) is being launched as a pilot within the DHInfra.at infrastructure initiative. DHInfra.at is developing a national Machine Learning infrastructure primarily for Digital Humanities, with CLARIAH partners and DH research projects receiving priority access. GPU4U serves as an exploratory pilot to understand how computational resources might be utilized by a broader student community, while the infrastructure’s core mission remains focused on Digital Humanities research.

Addressing the GPU Access Gap for Students

As part of this pilot exploration, GPU4U is granted access to computational resources for select student projects. The following three use cases will be evaluated as the infrastructure is being set up:

Use Case 1: Supporting Resource-Intensive Student Projects: One workshop per semester where students from various faculties can present project ideas and apply for limited resource allocation. Approximately five selected projects per semester may receive temporary access to the DHInfra cluster (featuring 12x H200 GPUs and multiple L40s) for tasks such as LLM fine-tuning, VR simulations, or other computational workloads.

Use Case 2: Limited LLM Inference Access for Educational Purposes: One GPU may be allocated to provide controlled access to LLM inference (via Ollama and various models) through an API for specific educational use cases. This would enable experimentation with prompting strategies, context size, temperature, and other parameters within structured learning environments. The inference could be integrated into select courses, such as a pilot “Introduction to Databases” course, to explore how such tools might support learning in non-technical disciplines.

Use Case 3: Experimental VR-Based Teaching Support: This use case explores the potential for VR in teaching through streaming solutions, particularly in Game Studies and Engineering contexts, which could reduce dependency on individual high-performance workstations. Implementation would depend on local infrastructure availability, such as VR headset access, and remains subject to further evaluation.

 

PI: Jasmin Wachter (AICS) & Mathias Lux (ITEC)

Further Reading: https://www.dhinfra.at/

https://www.dhinfra.at/2025-09-09-gpu4u-pilot-use-case/

 

Start of the 1st International School on Bilateral AI

The 1st International Summer School 2025 on Bilateral AI held from July 7 to July 11, 2025 at the University of Klagenfurt, Austria, started today!

The program of the school comprises 24 hours of advanced lectures and practical sessions, providing a comprehensive introduction to the field of bilateral Artificial Intelligence, comprising symbolic and subsymbolic AI. The school was organized by the Training Unit of the Cluster of Excellence Bilateral AI, funded by Austrian Science Fund (FWF), that brings together leading researchers from six top Austrian universities and institutions.

The Summer School is co-located with AIRoV – the Austrian Symposium on AI, Robotics, and Vision – which starts tomorrow and the students have the opportunity to partially attend.

We are looking forward to an informative and exciting week!

Kulturpreis des Landes Kärnten für Patrick Rodler

Patrick Rodler wurde am 13. Dezember 2024 neben Josef Winkler, Cristina Beretta und Franz Hartlieb mit dem Kulturpreis des Landes Kärnten geehrt.

Peter Kaiser dankte den Kärntner Kunst- und Kulturschaffenden für ihre „fast seismographische, kritische Reflexion der politischen und gesellschaftlichen Vorgänge im Land und darüber hinaus“. Die Kulturpreisverleihungen sollen auch Anerkennung für das vielfältige Wirken im Kulturland Kärnten sein. Neben Patrick Rodler konnten sich auch Josef Winkler, Cristina Beretta und Franz Hartlieb über den begehrten Kulturpreis des Landes Kärnten freuen.

Patrick Rodler, geboren 1984 in Klagenfurt, ist Privatdozent am Institut für Artificial Intelligence und Cybersecurity an der Universität Klagenfurt. Er studierte Technische Mathematik und Angewandte Informatik und promovierte in Informatik, jeweils mit Bestnoten (summa cum laude). 2023 habilitierte er sich in Informatik mit dem Schwerpunkt Künstliche Intelligenz. Seine Forschungsinteressen umfassen die Qualitätssicherung und Fehlerbehebung in Systemen wie Software, Hardware, Datenbanken, Robotern oder Fahrzeugen, die Lösung von Such- und Planungsproblemen, die computerbasierte Wissensdarstellung und -verarbeitung sowie maschinelles Lernen. Neben Auszeichnungen für seine Master- und Doktorarbeit erhielt er einen Preis für exzellente Lehre der Universität Klagenfurt. Im vergangenen Jahr war er auf allen internationalen Top-Konferenzen im Bereich Künstliche Intelligenz eingeladen, um seine Arbeiten zu präsentieren.

Insgesamt wurden im Dezember 2024 13 Preise mit einer Gesamtdotierung von 91.000 Euro wurden vergeben. Das Land Kärnten folgte damit den Empfehlungen des Kärntner Kulturgremiums. Die Preisverleihung fand am 13. Dezember 2024 in der Carinthischen Musikakademie Ossiach statt.

Foto Copyright: LPD Kärnten/LH Peter Kaiser mit allen Kultur Preisträger-innen 2024

NEWS of Research Group Intelligent Systems and Business Informatics

Project SAELING – SAving Energy by Learning and ImproviNG

The project name SAELING stands for SAving Energy by Learning and ImproviNG logic-based optimization models. SAELING was launched in early May 2024 and is funded by the Austrian Research Promotion Agency (FFG) to the tune of around 1.2 million euros. The total project costs amount to around 1.8 million euros.

The metal processing industry requires a considerable amount of energy, especially for sawing, grinding and milling. Using artificial intelligence methods, researchers are now hoping to increase efficiency by 20 to 30 per cent. The SAELING project is realised jointly by the University of Klagenfurt, the KU Leuven and the industrial partners Voestalpine and Siemens and is funded by the Austrian Research Promotion Agency FFG at a cost of around 1.2 million euros.

Voestalpine uses around 2,500 sawing, grinding and milling machines in its industrial plants. These consume approximately 21 GWh per year, corresponding to the electricity consumption of around 4,750 average Austrian households.

“Metal processing machines on the factory floor fulfil a variety of tasks. At present, the question of which machine should be used for which task and when has yet to be definitively resolved,” states Gerhard Friedrich, head of the SAELING project at the Department of Artificial Intelligence and Cybersecurity at the University of Klagenfurt. “We need to take many factors into account in order to develop strategies for sawing, grinding and milling in these kinds of workshops in a way that saves energy and resources wherever possible. Considering and simulating these factors along with their full impact is beyond the capabilities of human reasoning. In particular, the behaviour of these machines cannot be described with sufficient precision, but rather it has to be learned for the purpose of optimisation.”

Artificial intelligence methods are now set to significantly reduce energy consumption thanks to more efficient use, as Gerhard Friedrich goes on to explain: “Approaches such as reasoning, optimisation and machine learning will be put to use.”

The results from SAELING should facilitate analogue savings in other production areas. In addition to CO2 emissions, it should also be possible to reduce lubricant consumption, for example. It is intended that the tools developed in the project will be adaptable and can be extended to other areas of application, e.g. at SAELING’s partner Siemens.

For further information visit our HP SAELING and the Magazine Hi!Tech. of Siemens, one of our cooperation-partners.