Competence area Artificial Intelligence
The "Artificial Intelligence" (AI) competence area at the SICP researches and develops AI-based solutions that combine technical innovations with social and societal challenges. Our interdisciplinary approach integrates the latest technologies such as machine learning (ML), natural language processing, knowledge graphs and mathematical optimisation with social, ethical and legal issues in order to create sustainable and responsible AI systems. Accordingly, we focus on the development and evaluation of AI-based methods and systems to support and/or automate decision-making. We attach particular importance to the acceptance as well as the economic and ecological benefits of AI in various fields of application. For example, in application-oriented projects ranging from Industry 4.0 to mobility and energy in order to develop AI solutions with real added value.
Subject areas
This is an excerpt of current topics that are continuously expanded and updated.
- Machine learning/deep learning
- Natural language and language processing (incl. large language models)
- Computer vision
- Explainable AI
- Knowledge graphs
- Integration of mathematical optimisation and ML
- Data-driven decision making
- Data literacy
Fields of application
Mobility - Using AI to create an efficient mobility system
AI-based systems are revolutionising the field of mobility by optimising the flow of traffic in cities and regions and making logistics more efficient. This includes intelligent traffic management and logistics systems that reduce traffic congestion, shorten delivery times and minimise environmental impact.
Energy supply & smart grids - AI applications for smart grids, predictive maintenance and integrated energy systems
AI can be used to make modern energy systems more efficient and sustainable. AI-based technologies optimise the operation and maintenance of electricity grids and support the development of smart grids, which can react flexibly to electricity demand and energy supply. These systems make it possible to intelligently connect different energy sectors - such as electricity, heat and mobility - and promote the use of renewable energies.
Industry 4.0 - ML for predictive maintenance, energy-efficient production and CO₂ reduction
The use of machine learning makes production smarter, more resource-efficient and more sustainable. Predictive maintenance ensures higher machine availability, energy-optimised control reduces resource consumption and targeted CO₂ reduction contributes to environmental and climate protection. This not only brings industrial companies efficiency gains, but also strengthens their sustainability and competitiveness.
Goals and visions
Our aim is to research AI solutions that address both technological and social challenges. In addition, we want to strengthen human-AI collaboration for intelligent and responsible decision-making processes and develop new AI-based solutions in collaboration between science, business and society.
Director
> Wirtschaftsinformatik, insb. Data Analytics
Head - Professor
Office: Q2.457
Phone: +49 5251 60-5100
E-mail: oliver.mueller@uni-paderborn.de
Manager
> Software Innovation Campus Paderborn (SICP)
Coordinator - PostDoc - R&D Manager - Digital Business
Office: ZM2.A.03.25
Phone: +49 5251 60-5240
E-mail: weskamp@sicp.de
University lecturers involved
> Databases and Electronic Commerce
Section Owner - Professor
Office: F2.217
Phone: +49 5251 60-6662
E-mail: stb@uni-paderborn.de
> Communications Engineering / Heinz Nixdorf Institute
Head - Professor - Head of Department of Communications Engineering
Office: P7.2.05.3
Phone: +49 5251 60-3626
E-mail: haeb@nt.uni-paderborn.de
Professor
Office: E2.321
Phone: +49 5251 60-3275
E-mail: tobias.matzner@uni-paderborn.de
> Informatik Rechnerbetrieb (IRB)
Head - Professor
Office: F1.225
Phone: +49 5251 60-1761
E-mail: axel.ngonga@uni-paderborn.de
> Data Science for Engineering
Section Owner - Junior Professor
Office: O4.213
Phone: +49 5251 60-5021
E-mail: sebastian.peitz@uni-paderborn.de
Office: Q2.463
Phone: +49 5251 60-3115
E-mail: guido.schryen@uni-paderborn.de
> Machine Learning and Optimisation
Head - Professor
Office: FU.231
Phone: +49 5251 60-6309
Phone: +49 1606675582
E-mail: heike.trautmann@uni-paderborn.de