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Clusters of Excellence
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18 Results
3DMM2O
3D Matter Made to Order
In the Cluster of Excellence 3D Matter Made to Order (3DMM2O), we pursue the vision of turning digital blueprints into reality using scalable 3D additive manufacturing. This form of shaping matter, in which one locally adds material rather than subtracting it, has become a powerful tool at the macroscale. Our unique selling point is that we bring this technology towards the molecular scale, to answer previously inaccessible questions in the life and engineering sciences and enable new applications.
BlueMat
BlueMat: Water-Driven Materials
Biological materials achieve an exquisite diversity and functionality through just a small number of abundant chemical elements. While engineering materials primarily use specific, often unsustainable, chemical compositions to realize their functions, nature achieves unparalleled functionality through optimized architectures that span multiple length scales. Water, with its ubiquity and unique structural dynamics, plays a pivotal role as a “working fluid” in shaping the properties and functionality of nature’s materials.
CARE
CARE: Climate-Neutral And Resource-Efficient Construction
The construction sector consumes vast amounts of energy and resources, and is a major contributor to greenhouse gas emissions. It also suffers from low productivity and strenuous working conditions, while facing labor shortages and having a severe impact on the environment in terms of waste, noise, and other emissions. To mitigate global warming while still providing people with affordable homes and infrastructure that are resilient to the consequences of climate change, the construction industry requires a disruptive transformation.
CASA
Securing the Digital Society
In recent years, cyberspace has embraced an ever-larger share of our lives, and the notion of the digital society has become a reality. At the same time, we observe massive qualitative and quantitative shifts in the cybersecurity landscape. The attack surface has expanded dramatically, with adversaries targeting a broad range of entities, including hospitals, government services, higher education institutions, and critical infrastructure. This is further aggravated by the evolution of nation-state attackers pursuing geopolitical goals such as election interference. Finally, the emergence of disruptive technologies like generative AI, quantum computing, and the growing popularity of blockchains and cryptocurrencies further amplifies the digital risks. This dramatic change in the cybersecurity landscape poses a fundamental threat to our society, jeopardizing democratic processes, critical infrastructures, and citizens’ privacy and well-being alike.
CeTI
Centre for Tactile Internet with Human-in-the-Loop
CeTI²'s vision is to enable humans and machines to interact in near real time in globally distributed real and virtual environments. The cluster aims to develop innovative technological solutions to global challenges such as pandemics, demographic change, skills shortages, climate change, and geopolitical uncertainties.
Hearing4all.connects
Innovating Hearing Health Technology from Ear to Brain to Society
Hearing loss, the most common sensory disease, impacts communication, cognition, brain health, social participation, and well-being. As a leading consortium with unmatched interdisciplinary expertise in hearing research, we uncovered the effects of hearing loss on brain and cognition during previous funding phases and developed pioneering biomedical and engineering solutions for personalized hearing care. Although we have substantially advanced hearing loss treatment, we are still far from fully restoring the richness of natural hearing. This is due to a lack of insights into the links between molecular and auditory profiles necessary for causal therapies, and a scarcity of truly transformative technological approaches for rehabilitative treatment of hearing loss. However, with recent developments in genetics, data science, AI and health technologies, we now have an unprecedented opportunity to address these limitations.
IntCDC
Integrative Computational Design and Construction for Transformative Architecture
The Cluster of Excellence Integrative Computational Design and Construction for Transformative Architecture (IntCDC) aims to lay the methodological foundations for the urgently needed, future-proof and climate-positive transformation of the way buildings are designed and constructed. Based on interdisciplinary and integrative research, IntCDC seeks to harness the full potential of digital technologies to address the severe environmental, economic and social challenges that the building sector is facing. To do so, IntCDC bundles the internationally recognised competencies of the University of Stuttgart, the Max Planck Institute for Intelligent Systems and Bauhaus Earth across the fields of architecture, structural engineering, building physics and engineering geodesy, manufacturing and systems engineering, computer science and robotics, social sciences, humanities and economics.
IoP
Internet of Production
The vision of the Internet of Production (IoP) is to enable a new level of crossdomain collaboration by providing semantically adequate and context-aware data from production, development and usage in real-time, on an adequate level of granularity.
livMatS
Living, Adaptive and Energy-autonomous Materials Systems
The vision of the livMatS-Cluster is to merge the best of two worlds, the biological and the tech-nological realm, to develop living adaptive energy-autonomous Materials Systems.
Machine Learning
New Perspectives for Science
Machine learning is changing science even more profoundly than we imagined only a few years ago. While it has proven useful in tackling individual, long-standing scientific problems, recent developments suggest even more far-reaching possibilities. Foundation models, trained on vast datasets, provide general purpose representations suitable for a wide range of downstream tasks, and have already revolutionized tasks involving language. Diffusion models allow the generation of samples from complex probability distributions, and modern programming frameworks make it possible to implement scientific theories as part of machine learning workflows. Thus, machine learning is creating new possibilities in nearly all stages of scientific discovery and inquiry. At the same time, machine learning methodologies have obvious shortcomings regarding reliability, robustness, and interpretability, and come with risks, challenges, and blind spots.