Interdisciplinary Computing and AI
The University of Münster offers an exceptionally broad range of academic disciplines, facilitating collaboration between computer science, the natural sciences, the humanities and the social sciences in research, teaching, and knowledge transfer. This forms an excellent foundation for the Emerging Field "Interdisciplinary Computing and AI". Successes in theoretical development and algorithm design, as well as a wide range of interdisciplinary collaborations, are driving innovative research questions and cross-disciplinary applications within a dynamic research environment.
Digital progress increasingly raises socio-ethical questions and presents new societal challenges, which are being studied at the University of Münster in particular by the social sciences and legal studies. Their findings, in turn, feed back into theoretical computer science and algorithmic research. In this way, the University of Münster is one of the few institutions that covers all aspects of computer science and AI – including ethical, legal, and societal dimensions and their integration into a broad range of academic disciplines.
This is reflected in a wide array of research centres, initiatives and groups that transcend traditional disciplinary boundaries and connect cutting-edge digital developments across fields. The spectrum ranges from theoretical foundations explored in the Cluster of Excellence “Mathematics Münster”, to applications in the natural sciences – for example in the Priority Programme 2363 "Molecular Machine Learning" or the Collaborative Research Centre 1459 "Intelligent Matter" – and extends to the humanities and social sciences, as exemplified by the "Center for Digital Humanities". These activities are complemented by innovative teaching formats, such as the interdisciplinary programme InterKIWWU.
Research Focus "AI for Biomedical Data Analysis"
The availability of increasingly powerful AI techniques, combined with an ever-growing volume of data, has led to a new class of methodologies for studying biomedical questions. This is particularly evident in the case of image and video data, which are routinely collected in biological and medical contexts. Given the complexity of such biomedical data – including physiological, genetic and clinical records – meaningful analysis is often only possible through computational methods.
On the one hand, the University of Münster benefits from strong fundamental research in biology and one of Germany’s leading university hospitals, which together ensure the generation of cutting-edge data and a wealth of new research questions. On the other hand, an outstanding Department of Mathematics with the Cluster of Excellence Mathematics Münster and several highly interdisciplinary computer science units provide an ideal environment for developing and applying innovative AI algorithms in biomedical image and data analysis. This interdisciplinary collaboration – exemplified by research centres such as the Multiscale Imaging Centre (MIC) and the Cells in Motion Interfaculty Centre – forms the basis for a distinctive research focus on biomedical data analysis using state-of-the-art AI technologies.
Research Focus "Exploring Matter and Molecules via Interdisciplinary Computing"
The complexity of contemporary physical, chemical and pharmaceutical data calls for modern approaches to studying the dynamics and interrelations of matter and molecules. The latest developments in AI, for instance, can be used to design adaptive and intelligent matter or to identify new molecules for pharmaceutical applications. The strong Departments – Chemistry and Pharmacy on the one hand, and Physics on the other – provide an excellent foundation for computer-aided developments, which are at the forefront of pioneering research at the University of Münster. This strength is reflected in numerous collaborative research networks, such as SFB 1459 and SPP 2363. These initiatives are embedded within interdisciplinary research centres such as the Center for Multiscale Theory and Computation, the Center for Nanotechnology (CeNTech), the Center for Nonlinear Science (CeNoS), the Center for Soft Nanoscience (SoN) and the cryoEM Center. The integrative research carried out across these networks offers outstanding opportunities to explore and exploit matter and molecules through scientific computing and AI methods.
Research Focus "Data Science Theory"
The field of Data Science is often characterised by the application of algorithms, such as machine learning, to diverse data types with specific applications. The underlying theory of data science is based on complex mathematical frameworks (e.g. stochastics, optimisation, etc.), highly parallelised computing, and memory-efficient management of vast volumes of big data. Similarly, many black-box models call for new strategies to explain the underlying dynamics of high-dimensional computations (often referred to as Explainable AI, or XAI). Building on a strong mathematical foundation and a wide range of computer science expertise, this focus area addresses the theoretical aspects of data science. While these developments still require implementation with practical applications in mind, the emphasis is on the mathematical foundations of the algorithms themselves, paving the way for the next generation of data processing strategies.
Research Focus "Digital Humanities"
The term Digital Humanities denotes a highly dynamic field of research that combines the epistemological concerns of the humanities and cultural studies with the computational methods of computer science. Its primary objective is the structured collection and analysis of the entire spectrum of cultural artefacts as data. Consequently, the range of tools available for addressing research questions in the humanities has been expanded to include digital methods and instruments. Simultaneously, the digital research process itself fosters innovative approaches and modes of access, broadening the scope of insight. The source materials of humanities research – such as historical documents, works of art, as well as speech acts and social structures – are digitised, annotated, and made virtually accessible. Building on these enriched data formats, both detailed processing (e.g., as digital editions) and the networking and computer-assisted analysis of large and complex corpora (e.g., text mining, stylometry, topic modelling, network analysis) are made possible. At the University of Münster, Digital Humanities are firmly embedded across various disciplines in both research and teaching, benefiting from a strong and pioneering service infrastructure through the Service Center for Digital Humanities, based at the University and State Library of Münster.