Research

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.

The Earth System Science research team at CASUS tackles problems at the interface spatial ecology, hydrology, and anthropogenic change to understand the dynamics of biodiversity in the Anthropocene. It combines sophisticated models of spatial ecological processes with multispecies datasets from around the world by leveraging the latest techniques in data science, machine learning, and high-performance computing.

Our research group uses advanced computational methods to design sustainable materials for diverse applications, including semiconductor devices, spintronics, neuromorphic devices, thermoelectrics, and energy storage devices. We use scalable machine learning frameworks to improve density functional theory simulations, connect microscopic and mesoscopic simulations, and model advanced material properties. Our techniques incorporate artificial intelligence to improve efficiency, enabling us to study phenomena as diverse as magnetostructural phase transitions in ultrafast magnetic memory devices and electron transport in nanoscale electronics.

CASUS contributes to understanding life and the organization of living matter. Research at CASUS aims to enable next-generation biology and to improve the mechanistic understanding and control of living systems. To achieve this, the group uses tools like virtual and augmented reality as well as computational prediction.

The department supports CASUS researchers in all aspects of scientific computing and data-driven research. It also conducts research on specific topics of its own. The members of the core come from various scientific disciplines and complement the subject-specific expertise of the scientific staff with methods, tools and extensive computer science knowledge.

Automated decisions made by self-moving vehicles require networking with the surrounding infrastructure and between the moving vehicles. CASUS’ use of machine learning methods in combination with behavior models and sensor data will create a digital image of the current surroundings for navigating the vehicle. This enables, for example, better decision-making about the behavior of other vehicles.

The group models, simulates and visualizes the dynamics of particles and radiation phenomena that are of interest when investigating the physics of laser particle acceleration. The aim is to create models for innovative and compact sources of radiation that make the best use of the ultra-strong electromagnetic fields being created by the interaction of light and matter at relativistic intensities.

In the Theoretical Chemistry group, we explore innovative materials, with a particular emphasis on two-dimensional systems, for use in energy storage and generation, catalysis, isotope separation, and nano(opto)electronic devices. To achieve this, we utilize a range of quantum-mechanical techniques to analyze the structural, electronic, vibronic, and optical characteristics of materials

Our group develops novel methods and concepts to tackle problems from quantum many-body theory. The focus of our research is given by the simulation and diagnostics of warm dense matter, which requires a rigorous treatment of the complex interplay of effects like Coulomb coupling, quantum degeneracy, and strong thermal excitations. In addition, we apply our methodologies to other many-body systems such as ultracold atoms and electrons in quantum dots.

The beauty and fascinating enigmatic nature that complex systems embody might be the driving force behind the ambitions of many scientists in their realm of scientific research. As it turns out, resolving the computational bottleneck of scientific questions being addressed today often appears as the central issue. Our research mission is to accumulate valuable insights in applied and numerical mathematics, especially in their practical applications to these real-world challenges. Hereby, a fulfilling aspect of our work is the ability to construct a bridge between the abstract, theoretical world of pure mathematics and the practical, empirical world of the sciences. This connection serves as a source of immense satisfaction and a driving force behind our ambitions in the realm of scientific research.

The interdisciplinary group consists of scientists seeking to understand and quantify how the components of complex living systems like organisms or cells interact among each other and with the environment – and how these behaviors drive higher-level processes. Of particular interest are spatio-temporal patterns and their ecological and evolutionary implications. The team combines mathematical modeling and different classes of empirical datasets to realize its ambitions.

Machine Learning for Infection and Disease (MLID) group aims to develop novel computational methods to facilitate our understanding of Infection Biology and Disease Biology. To achieve these aims we combine expertise in Deep Learning, Generative AI, Computer Vision, Biomedical Image Analysis, Simulations and Systems Biology. We develop novel algorithms, tasks and benchmarks to power the new generation of Biomedical AI and Biomedical Discovery.