AIM for for Science – Unlocking the Potential of Artificial Intelligence for Research
Advances in computing power, the growing availability of large datasets, and recent developments in machine learning have made artificial intelligence (AI) a key driver of modern research. Today, AI supports a wide range of scientific activities, from analyzing complex data and designing experiments to developing data-driven models and generating new hypotheses. Its integration into the scientific discovery process is opening up new opportunities for innovation and knowledge creation across disciplines.
Our „AIM for Science“ (Artificial Intelligence Münster) initiative seeks to bring together AI expertise and research needs from across the University of Münster. At its core is the close collaboration between methodological and application-oriented disciplines, creating new impulses both for the advancement of AI and for its use in addressing scientific challenges. By connecting researchers and research groups within the university and building strategic partnerships with external researchers, institutions, and stakeholders, AIM for Science aims to foster an environment that lowers barriers to the adoption of AI, strengthens interdisciplinary exchange, and creates new opportunities for collaboration both within and beyond the university.
Workshop series "AIM for ..."
The AI Map
Following the motto “Navigating the AI Landscape @ Münster University”, our AI Map provides an overview of the diverse AI ecosystem at Münster University. It includes research groups that develop and apply AI, as well as those exploring its implications for society, law, ethics, and education. In addition, the map features institutions, services, and contact persons related to artificial intelligence across the university.
Upcoming Workshop: AIM for Sustainability
Artificial intelligence is becoming an increasingly important tool for addressing sustainability challenges. From analyzing environmental systems and optimizing infrastructure to monitoring and managing natural resources, AI methods enable new ways of extracting knowledge from data, improving predictions, and supporting informed decision-making.
The landscape of AI methods is as diverse as the sustainability challenges they address. Drawing from this wide range of research opportunities, the workshop’s Keynote Talks highlight two particularly promising areas: GeoAI and AI for sustainable energy system operation. The keynotes are complemented by Spotlight Talks showcasing current research activities at the University of Münster and highlighting opportunities for interdisciplinary exchange and future collaborations. Beyond that, the workshop also provides space for critical reflection on the role of AI in sustainability research by inviting discussion of dominant technological narratives.
Bringing together researchers from diverse disciplines, the workshop is designed to spark new ideas, encourage fresh perspectives, and strengthen AI for sustainability research at the University of Münster. We warmly invite all interested researchers, including PhD candidates and postdoctoral researchers, to join the event.
When: 14 September 2026, 12:30 – 17:45
Where: GEO 1 Building, Heisenbergstraße 2
Registration: Register by 31 August via Indico.
Contact: Katrin Schmietendorf (CDSC), Carolin Bohn (ZIN), Fynn Schmidt (Ulysseus).
For the schedule and abstracts, please see the detailed program.
The workshop is co-organized with the Center for Interdisciplinary Sustainability Research (ZIN). International participation in the event is enabled by the Ulysseus European University Alliance.
Upcoming Workshop: AIM for the Social Sciences
Planning for this workshop is already underway. It will take place in winter 2026, and information on the date and programme will be published here as soon as it becomes available. If you would like to stay informed, you can join our mailing list to receive updates on this workshop as well as on other CDSC activities.
Past Workshop: AIM for the Brain
From artificial to natural intelligence: As machine learning systems become increasingly powerful at recognizing patterns and modeling complex dynamics, they are also being used more and more in cognitive neuroscience to investigate fundamental questions: How does the brain generate thoughts, memories, perception, and actions? And how can data-driven models help uncover the underlying principles of these processes? Ultimately, could artificial intelligence help us better understand natural intelligence?