Research

IASI (Infrared Atmospheric Sounding Interferometer) and IASI-NG (IASI-Next Generation) are key satellite instruments of the EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) Polar System. The instruments measure thermal nadir spectra with high spectral and horizontal resolution, twice daily global coverage, and a multi decadal mission continuance. This project explores these excellent opportunities for atmospheric research on different scales by retrieving the distribution of multiple atmospheric trace gases from the measured IASI spectra. The large trace gas data sets are the basis for investigating manifold atmospheric processes on weather as well as climate scales.

An optimum and selective materials design based on computational approaches is essential in order to avoid time-consuming and expensive experiments and drive novel applications in sensing, catalysis, and electronic components. Within this concept, (a) the optimization of nanoporous materials made of functionalized gold surfaces with self-assembled monolayers, as well as (b) the design of alloying materials were investigated. The investigations are directed towards (a) heterocatalysis and biosensing, as well as (b) alloys for strong and highly conducting electronic components were seeked. This research was performed using quantum-mechanical and atomistic simulations occasionally in combination with Machine Learning approaches.

Photonic materials made of carefully designed structures can manipulate light in extraordinary ways, enabling applications in imaging, sensing, information processing, and beyond. Designing such materials involves solving inverse problems: instead of determining how a given structure interacts with light, we start with a desired optical response and work backward to find the optimal material configuration. This requires advanced numerical approaches, both model- and data-driven, and a high-performance computing infrastructure to efficiently explore vast design spaces and achieve precise control over light propagation. Our NHR project allows us to tackle these challenges.

Data-Driven Virus Discovery (DDVD) is revolutionizing the way novel viruses are discovered. Being independent of the collection and processing of biological samples, DDVD allows for screening massive amounts of next generation sequencing (NGS) data for the presence of known and unknown viral genome sequences. We utilize DDVD to analyze 1+ million of public NGS datasets from the Sequence Read Archive (SRA) and find 150+ thousand sequences of viral origin. We use these data to assess the risk of spillover into humans across the RNA viruses and to study various aspects of viral evolution across geologic time scales.

Aerosol-cloud interactions (ACI) are among the most uncertain processes in numerical weather prediction models. The effects of aerosols on clouds and precipitation vary significantly depending on the cloud type. Generally, high aerosol concentrations are assumed to activate more aerosol particles as cloud condensation nuclei (CCN), resulting in a larger number of smaller cloud droplets. This smaller droplet size suppresses the onset of precipitation in warm clouds by reducing the collision-coalescence process, leading to longer cloud lifetimes. Under polluted conditions, the increased water load at the freezing level can release additional latent heat, potentially invigorating convective clouds and enhancing rainfall. However, recent

The evolution of organic semiconductors (OS) has revolutionized the electronics industry, from organic light-emitting diodes (OLEDs) to organic solar cells. Despite their advantages like a low cost, flexibility, sustainability, OS materials face significant challenges due to their inherently low conductivity. To address this, conductivity doping - adding specific molecules to enhance electrical conductivity - is used. While the doped layers themselves do not emit light, they enable efficient charge injection and extraction in OLED devices, ensuring optimal performance in the active layers where light is generated. Traditionally, designing new dopants relies on a trial-and-error approach, which often overlooks possible design strategies. In

Over the past decades, large-scale computer simulations have grown to become one of the most powerful approaches to study the interior of Earth-like planets. Geodynamical models are used to investigate the evolution and distribution of the temperature inside the planet that ultimately affects its structure and the way the planet cools over time. Combined with data obtained from planetary missions and laboratory experiments, these models help us to improve our understanding of the history and current state of planets in our Solar System and beyond. These models can teach us about the formation and evolution of planetary environments

The liver produces bile, which the intestine uses for digestion. For the transport of bile, the liver relies on a network of microscopic tubings, known as bile canaliculi, formed by liver cells called hepatocytes. When the outflow of bile to the intestine is blocked, it collects in the liver and can lead to serious liver disease. Researchers at the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) in Dresden together with experts from the Carl Gustav Carus University Hospital (UKD) and the Department for Information Services and High Performance Computing (ZIH) at TU Dresden as well as further clinics in Germany and Oslo University Hospital in Norway found that high pressure in the bile canaliculi alters the structure of

We aim to make sophisticated, computationally demanding bio-image analysis workflows more accessible to researchers who have little or no programming background. Thus, we organized a course that leveraged the Jupyter Hub interface on an HPC cluster in conjunction with custom Singularity containers, each tailored to the specific needs of individual projects. This setup enabled seamless execution of tasks such as three-dimensional image deconvolution, deep- learning–based segmentation (U-Net), and denoising algorithms, all within a user-friendly environment.
Participants in the course successfully ran these resource-intensive workflows without needing to install or configure complex dependencies. These results demonstrate the reproducibility

Proteins are the workhorses of cells, driving all processes essential for survival. Each protein folds into a specific shape, uniquely tailored to its sequence and function. Predicting protein structures from sequences has long been one of biology’s greatest challenges. Recent breakthroughs in computational methods, like AlphaFold, which earned the 2024 Nobel Prize in Chemistry, have revolutionized this field.