CASUS - Center for Advanced Systems Understanding
CASUS is a new research institute established in Görlitz in 2019. It combines methods from mathematics, systems theory, data science and scientific computing at a single location to rethink data-intensive systems research across individual disciplines.
Vision
In the future, understanding complex, networked systems will make a decisive contribution to mastering society's important challenges. For the first time in human history, novel digital methods will enable us to understand this complexity and master it by networking various scientific disciplines.
Mission
CASUS - Center for Advanced Systems Understanding(1) is unique in Germany as a center for digital interdisciplinary systems research. It is expected to occupy a leading international position in this up-and-coming field of research.
Digital interdisciplinary systems-research researches and develops the latest and most innovative methods from mathematics, modelling, simulation, data and computer science to solve questions from such diverse areas of systems research as earth systems research, systems biology, digital health or materials research.
CASUS aims to bring together the best scientists from these fields at a joint institute in order to develop visionary ideas in interdisciplinary teams on how to master the complex challenges of the future with digital methods.
Summary
CASUS is to become the centre for digital interdisciplinary systems research in Germany. CASUS aims to create digital, dynamic "worldviews" of complex systems that combine large amounts of data about these systems with novel methods of modelling such systems in order to create a digital image of complex reality based on systems and their interactions and thus be able to make predictions. The understanding and predictability of the development of complex systems will become increasingly important in the coming years, e.g. for a better understanding of the development of complex organisms, the long-term development of the Earth system and the development of novel materials, and thus become increasingly important for research as well as for business and decision makers.
The underlying assumption of CASUS is that in the future this knowledge and understanding of the complexity and diversity of the world will experience disruptive change through the use of novel digital methods from big data and large-scale simulations. Systems research will play a central role in this, in order to conduct joint methodological research across scientific disciplines. CASUS will rethink the use of state-of-the-art technologies and methods. These should make the best possible use of existing technologies, decisively advance systems research with novel algorithms and design these methods in such a way that they are available to the broadest possible circle of scientists without specialist knowledge.
An institute with this orientation has not existed before, which is why CASUS is an attractive location for internationally leading experts who come from the various disciplines of systems research and its applications, method research for modelling and data analysis as well as mathematics and work together in interdisciplinary teams. CASUS aims to bring together the best minds for cutting-edge research in digital systems science in one place. In doing so, great importance is attached to innovative and unorthodox research approaches in order to overcome historically grown structures of individual disciplines and to promote interdisciplinary solutions. An essential part of the CASUS concept is an attractive international fellowship and workshop program for top international scientists.
Research Focus: Machine Learning for Materials Design
The research group uses advanced computational methods to design sustainable materials for diverse applications, including semiconductor devices, spintronics, neuromorphic devices, thermoelectrics, and energy storage devices. Hereby scalable machine learning frameworks are used to improve density functional theory simulations, connect microscopic and mesoscopic simulations, and model advanced material properties.
The techniques incorporate artificial intelligence to improve efficiency, enabling the study of phenomena as diverse as magnetostructural phase transitions in ultrafast magnetic memory devices and electron transport in nanoscale electronics.
The research topics at a glance:
- Scalable machine learning for electronic structure calculations
- Magneto-structural phase transitions
- First-principles electronic transport properties
- Electronic structure methods and machine learning
Research Focus: Earth System Research
Research into the entire Earth system must be performed in order to understand the connections between geology, climate, ecology and human influence in the interplay of these complex systems. In this context, an ever-increasing amount of sensor data has to be brought together in real time and compared with models. Improving the predictive power of these models will influence political decisions, economic developments and people's daily lives.
Data- and computation-intensive computer models are developed at CASUS that allow the ecological, hydrological and economic effects of global change to be studied in high spatial and temporal resolution and in their complex interactions.
CASUS will provide predictions for entire ecosystems and their ecosystem functions for the next 50 to 100 years, including their:
- biogeochemical cycles,
- water quality and quantity,
- biomass production and agricultural yields, as well as
- biodiversity,
allowing the quantitative analysis of the overall diversity of event chains and of feedback mechanisms between them, including the overall economic costs and ecological consequences.
Research Focus: Systems Biology
In systems biology the quantum mechanical and quantum chemical nature of the molecules that make up the individual cells, proteins and other functional components of organisms is to be linked to the development of the entire organism. In addition, data must be obtained in vivo, i.e. on the living organism, which is constantly changing and constantly in disequilibrium. In addition, the subsystems of an organism are networked in such a way that they can only be understood in interaction. Huge amounts of data from imaging processes, genetics, biochemistry and modelling must be combined and integrated into a common picture of the origin and function of organisms.
CASUS performs basic research to enable the mechanistic understanding of biological processes and the control of living systems. This includes:
- virtual and augmented reality for the laboratory of the future,
- computational prediction and control of biological processes, and
- learning and inference of computable models from microscopy data.
CASUS’ methods and algorithms will allow us to deeply understand living matter and the processes of life in their inner function across multiple scales, from molecules to tissues, based on terabytes per day of data obtained from high-resolution 3D microscopes.
Research Focus: Digital Health
CASUS focuses on technologies and solutions for secure, intelligent, and sustainable data management in digital health. The first application scenario addressed by CASUS is to help medical professionals in making the optimal decisions in cancer treatment.
When it comes to implementation, CASUS is pursuing the idea of making machine learning in digital health possible via a federated and scalable approach. In this way, analysis, model building, and knowledge extraction can be realized close to the patient data.
CASUS has taken the lead in developing an open-source solution for federated machine learning and artificial intelligence in digital health. This approach will allow artificial intelligence models to be trained on patient data from different sites and the results to be consolidated into a single model.
Research Focus: Theory of Complex Systems
Chemical as well as physical processes are intrinsically associated with large length and time scales. Thus, an at least partially quantum mechanical description of such a many-body system is analytically only possible in very few exceptional cases. Instead, a statistical mechanical treatment with quantum mechanical methods that can be solved by modern massively parallel high-performance computers is required. The main task is therefore to devise and implement novel numerical techniques, which are as efficient as possible and yet, at the same time, qualitatively reproduce the correct chemistry and physics of the original system.
However, the focus is not solely on the development of new algorithms, but also to solve scientifically relevant questions of chemistry, physics, material sciences and biophysics. In general the main interest is the investigation of complex systems in condensed phases (liquids, solids and supramolecular systems). In particular, the research group focuses on studying aqueous systems such as water interfaces, water in confined geometries, biological relevant reactions in aqueous solution and the heterogenous “on-water” catalysis. Additionally, also sustainable systems and energy materials, specifically thin-film solar cells, polymer electrolyte fuel cells, lithium-sulfur batteries, novel hydrogen-storage materials, solid hydrogen, non-volatile phase-change materials and topological Weyl-semimetal-based catalysis are investigated.
CASUS open positions
Please find our current open positions here(2).
You are welcome to send an unsolicited application(3)
Participating institutions
Helmholtz Center for Environmental Research (UFZ)(4)
Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)(5)
Technische Universität Dresden / Center for Information Services and High Power Computing (ZIH)(6)
Funding sources
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Contact
Prof. Dr. Thomas Dae-Song Kühne
Director CASUS
Head Theory of Complex Systems
t.kuehne
hzdr.de
Phone: +49 3581 37523 107
Dr. Martin Laqua
Officer Communications, Press & Public Relations, Center for Advanced Systems Understanding
Communication & Media Relations
m.laquahzdr.de
Phone: +49 3581 37523 24
URL of this article
https://www.hzdr.de/db/Cms?pOid=57317
