CREATE_AT – Circular Business Models and Supporting Policies for Timber Supply Chains

(lead: Lena Silbermayr; 2024–2027; FFG) CREATE_AT supports Austria’s construction industry in its transition to a circular economy by promoting timber as a sustainable alternative to fossil-based materials. Aligned with the European Green Deal and UN SDGs, the project develops circular business models and policy recommendations to foster sustainable transformation.

COE 12 – FWF Cluster of Excellence Bilateral AI

(lead: Axel Polleres; 2024–2029; FWF) Combining sub-symbolic (machine learning) and symbolic (knowledge representation and reasoning) approaches, this cluster advances the foundations of Broad AI—systems able to draw their own conclusions and exhibit comprehensive cognitive abilities.

Future Foundation

(lead: Sarah Spiekermann) An interdisciplinary forum of distinguished experts developing guidelines for addressing urgent questions about the good life in a digital and AI-shaped world. It focuses on protecting human dignity and designing human-centered, democratic digital transformation.

Competence Center for Applied AI and Scientific Computing

A cross-disciplinary initiative uniting researchers from the Departments of ISOM, Finance, Accounting and Statistics, and Marketing. The center brings together AI researchers and practitioners to develop innovative solutions, translate research into applications, and assess the societal and organizational impact of AI.

Management of information, performance and finance

This cluster focuses on research about methods and approaches to manage, use and exchange information by governments in the policy, management and financial cycles. This may be within as well as between administrative organizations, but also across and between governments.

Manufacturing Foundation Model for Intelligent Production Systems

This project focuses on developing a large-scale foundation model tailored for smart manufacturing environments. By integrating multimodal industrial data—including process logs, sensor data, and textual production knowledge—the research aims to construct an AI-driven framework that supports adaptive decision-making, predictive maintenance, and intelligent process optimization. The project combines expertise in industrial engineering, artificial intelligence, and data […]

CCLS initiative

Center for Computational Life Science; networking initiative across the faculty of science to accomplish a stronger momentum and exploit synergies among fields.

Process Mining

Optimizing business processes is vital for achieving organizational strategic goals. This project explores advanced process knowledge using machine learning and computational intelligence. Ongoing works focus on process discovery, quality metrics, concept drift, interpretability and explainability, prediction and prescription, bias and fairness, LLMs, trace clustering, graph embeddings, sequencial association rules, semantic parsing, plain language, and OCEL, […]