Helmholtz Hosts A-C

Ansprechpartner

Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) - Bioinformatics and Omics Data Science Platform

Short summary of your group's research: We have a broad interest in gene regulation, specifically transcriptional regulation and association of transcriptional regulation with epigenomics. We are aiming to use data intensive computational methods to uncover patterns in gene regulation relating to cell differentiation and complex diseases.    

What infrastructure, programs and tools are used in your group? HPC, statistical and machine learning tools implemented in Python or R

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? Participant can learn domain specific data processing techniques and provide support in machine learning applications.

Ansprechpartner

Jülich Research Centre (FZJ) - Data Analytics and Machine Learning (IAS-8, Institute for Advanced Simulation)

Short summary of your group's research: We aim at advancing theory and application of data sciences with a focus on image processing and computer vision for imaging as a scientific measurement tool. Our interests range from use-inspired, method-driven theory to application-driven research.

What infrastructure, programs and tools are used in your group? We work with state-of-the-art methods and algorithms in machine learning and data mining; most of our implementations are in python, and we make use of gpu-parallelization for efficiency and scalability; we devise methods that support scientists in other fields in analyzing and handling their data

What could a participant of the HIDA Mobilty Program learn in your group? How could he or she support you in your group? You can learn more about how to train machine learning models, develop (variants of) such models, devise efficient algorithms, or use them to work with your data or with the data provided by our collaborators.

Ansprechpartner

German Cancer Research Center (DKFZ) - Dendritic Cells in Infection and Cancer

Short summary of your group's research: Our research group is interested in elucidating the role of different DC subpopulations, their development and basic function in immune activation by bacterial infections, inflammation, and cancer to improve the basic understanding of DC function in disease but also the potential of DCs as targets for therapeutic intervention. Another focus of our research group is the phenotyping of immune cells using spectral flow cytometry. This allows the detailed characterization of all immune cells in the blood and tissues of patients before and during therapy with the goal of identifying cell populations or biomarkers that can predict therapy response.

What infrastructure, programs and tools are used in your group? The main techniques are spectral flow cytometry and scRNA sequencing. With spectral flow cytometry about 40 proteins that are expressed on or in cells can currently be determined simultaneously at the single cell level.  We use tools like OMIQ for flow cytometry data and R for scRNA data analysis.

What could a participant of the HIDA Mobilty Program learn in your group? How could he or she support you in your group? We offer insights into single cell analysis of immune cells especially how bacterial infections modulate the dendritic cell development. We hope to get support regarding the molecular mechanisms responsible for the infection-induced impaired dendritic cell development by analyzing our scRNA seq & spectral flow cytometry data. 

Ansprechpartner

Helmholtz-Zentrum Berlin für Materialien und Energie (HZB) - Young Investigator Group Theory of Electron Dynamics and Spectroscopy

Short summary of your group's research: The electronic Schrödinger equation encodes the excited states of any material and their interaction with light may it be in electron dynamics or spectroscopy. We solve the equations in particular for nanomaterials accounting for their chemical environment. Particular interest lies in employing the recent methods of data science and quantum computation along with the traditional theories.

What infrastructure, programs and tools are used in your group? Different commercial and self-written quantum chemistry codes            

What could a participant of the HIDA Mobilty Program learn in your group? How could he or she support you in your group? Learn: Questions from the domain science quantum chemistry to data science, in particular handling of scarce data. Cutting-edge method development. Support: Overview knowledge on data science and experience in formulating questions properly for a data-driven solution.

Ansprechpartner

GEOMAR Helmholtz Centre for Ocean Research Kiel - Marine Biogeochemistry (Res Div) / Chemical Oceanography (Res Unit) / Trace Gas Biogeochemisty (Working Group)

Short summary of your group's research: My working group is working on the biogeochemical pathways and emissions of climate -relevant trace gases such as N2O, CH4, CO, DMS  and NO. Moreover, we run the Boknis Eck Time-Series Station (Eckernförde Bay, SW Baltic Sea) and the MEMENTO (the MarinE MethanE and Nitrous Oxide) database.  We are operating worldwide.

What infrastructure, programs and tools are used in your group? - Boknis Eck Time-Series Station (www.bokniseck.de); Boknis Eck underwater observatory
- MEMENTO database (ttps://memento.geomar.de)
- Various research vessels
- Lab based measuerments

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? We offer:
-  Work with time series data and databases (Boknis Eck, MEMENTO)
To want:
- Someone who to develop and realise new concepts for visualisation/integration/analysis of data sets. 

Ansprechpartner

German Aerospace Center (DLR) - Institute of Aerospace Medicine, Sleep and Human Factors  Research - Noise Effects Research

Three-sentence summary of your group's research: We investigate how air, rail and road traffic noise affect sleep, cognitive performance and annoyance. We conduct both laboratory studies as well as field studies in residents affected by transportation noise and derive exposure-response relationships for awakening probabilities and annoyance that are used to develop protection concepts for the affected population. Noise effects are being studied in healthy adults as well as in vulnerable individuals, e.g. children and elderly people.

What infrastructure, programs and tools are used in your group? To collect data on sleep quality and cardiovascular parameter, we apply polysomnography incl. EEG, ECG, pulse oximetry and actigraphy. Noise exposure data are obtained via acoustic measurements (e.g. by means of class-1 sound level meters). Survey data are obtained via postal or online surveys using, for instance, LimeSurvey. For data management and analysis, we use e.g. SOMNOmedics DOMINO, Matlab, R-Studio and SPSS as well as self-programmed and customized tools based on Matlab and LabVIEW. 

What could a guest researcher learn in your group? How could he or she support you in your group? By joining our group, you could learn about the manifold effects of environmental noise on human health and their underlying mechanisms. You would benefit from our expertise in data collection in controlled experimental designs in the laboratory and the field as well as in surveys. Furthermore, you could increase your knowledge in sleep measurement techniques, e.g. polysomnography, and apply your skills (incl. machine learning skills) in the analysis of cardiovascular reactions to noise (e.g. regarding heart rate variability, pulse transit time) and/or noise-induced reactions in sleep (e.g. vegetative-motoric reactions, identification of EEG arousals).
Expertise in bioengineering, psychology, medicine, biology, and related disciplines, with a special emphasis on skills in advanced statistical analyses, data visualization and machine learning are very welcome. We are looking forward to an open exchange of ideas and methods for the in-depth investigation of noise impacts on humans.

Ansprechpartner

German Center for Neurodegenerative Diseases (DZNE) - Modular High Performance Computing and Artificial Intelligence

Short summary of your group's research: The Modular High Performance Computing and Artificial Intelligence group works on building and improving computational architectures for growing data sets in neurodegeneration with the focus on memory-centric systems. Furthermore, we employ swarm learning for federated machine learning in many different applications. Another application of artificial intelligence  is the use of GANs and VAEs to create synthetic cohorts in a Helmholtz AI project.

What infrastructure, programs and tools are used in your group?

- We use high-memory systems (6TB+) for improving the processing of genomics data analysis applications using c/C++ and Rust.
- For ML applications, we have GPU ressoucres available at the institute and access to Jülich. Development is mainly done in Python using common libraries like Pytorch (Lightning) or Tensorflow.

What could a participant of the HIDA Mobilty Program learn in your group? How could he or she support you in your group? A guest researcher will join a highly interdisciplinary group at the intersection of computer science and medicine. We are well integrated with in the systems medicine research area and collaborate with clinical partners.  Projects can be either focused on the performance analysis and optimization of data processing task in genomics or on ML applications. Here the implementation of a swarm learning use case in a new domain would be option as well as work synthetic cohort generation.

Ansprechpartner

Jülich Research Centre (FZJ) - Institute of Neuroscience and Medicine (INM-2)

Short summary of your group's research: We use explainable AI on population-based studies to target neurodegenerative diseases, e.g. Alzheimer's disease.  Machine learning algorithms like decision trees or tree ensembles can learn patterns in data, and the explanations are therefore promising tools to address interactions of genes, environment and lifestyle with respect to a certain trait or disease. We seek to evaluate and use different methods of explainable AI with respect to scientific discovery tasks. 

What infrastructure, programs and tools are used in your group? Python, GitHub, access to various clinical and population-based databases like ADNI (the Alzheimer's Disease Neuroimaging Initiative), PET, hybrid MRI/PET and various laboratory techniques for preclinical and clinical research as well as research in basic neuroscience in a highly interdisciplinary environment.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? A guest researcher will support in evaluating explainable AI for scientific discovery, especially with respect to the interaction of genetics, environment and lifestyle in neurodegenerative diseases. 

Ansprechpartner

GEOMAR Helmholtz Centre for Ocean Research Kiel - RD3/OEB

Three-sentence summary of your group's research: Our laboratory conducts research on single-celled organisms known as bacteria and archaea that have a crucial role in the cycling of (micro)nutrients in both the photic and aphotic regions of the global oceans. Our research in the field of biological oceanography encompasses the integration of marine microbial ecology and biogeochemistry, operating under the overarching framework of physical oceanography. At GEOMAR, our research focuses on investigating microbial B vitamins, ectoenzymes, and the functional capabilities of microorganisms. We employ integrated multi-omics approaches, together with laboratory studies conducted at the Boknis Eck Time-Series Station in the Southwest Baltic Sea and during oceanographic cruises.

What infrastructure, programs and tools are used in your group? State-of-the-art molecular tools are employed in our lab facilities to conduct experiments involving these organisms. These tools include e-DNA analysis, molecular laboratory techniques, flow cytometry, microscopy, and the use of genetically modified organisms (GMOs). Metagenomic, -transcriptomic, and -proteomic analyses generate a vast amount of data, which can be effectively analyzed by leveraging the high-performance computing cluster available at Kiel University. The analysis of Underwater Vision Profiler (UVP) images is presently conducted using the Quantitative Imagery Platform of Villefranche.

What could a guest researcher learn in your group? How could he or she support you in your group? The visiting scientist will have the opportunity to actively participate in ongoing research and gain knowledge regarding the bioinformatic analysis of multi-omic and e-DNA datasets. In summary, this encompasses the implementation of essential software, the development of procedural frameworks, computational methodologies for data-driven microbiology, and the utilization of visualization techniques.

Ansprechpartner

German Aerospace Center (DLR) - AI4BuildingModeling at the Photogrammetry and Image Analysis Department

Three-sentence summary of your group's research:  The young investigator research group AI4BuildingModeling, led by Dr. Ksenia Bittner at the Photogrammetry and Image Analysis Department, focuses on cutting-edge deep learning approaches for geospatial data analysis. Our work covers building information extraction in raster and vector formats, 3D building modeling, Digital Surface Model optimization, Digital Terrain Model extraction, and super-resolution of Earth observation data from satellites like Sentinel-2 and Cartosat-1, and more. By integrating diverse multimodal data with advanced AI techniques, we enhance data accuracy, unlock new insights, and expand the potential applications of Earth observation data.

What infrastructure, programs and tools are used in your group? Programming is mostly done in Python, with deep learning developments primarily using PyTorch. We have access to GPU servers for accelerated computation, and our workflows are managed with Git for version control and Docker for containerization.

What could a guest researcher learn in your group? How could he or she support you in your group? 

We offer visiting researchers the opportunity to engage in innovative research at the intersection of geospatial data analysis and artificial intelligence. They will gain hands-on experience in developing deep learning models for 3D object and environmental representation modeling from multimodal data sources, enhancing satellite image resolution, and applying AI-driven approaches to geospatial challenges. Visiting researchers can contribute by designing novel AI architectures, optimizing existing pipelines, or exploring new applications of remote sensing data. They will also have the opportunity to work with high-performance computing resources and collaborate in an interdisciplinary research environment.

Ansprechpartner

CISPA - Helmholtz Center for Information Security - Machine Learning Research: Group on Secure, Private, Robust, INterpretable, and Trustworthy Machine Learning

Three-sentence summary of your group's research: Our group, the SprintML lab focuses their research on Secure, Private, Robust, INterpretable, and Trustworthy Machine Learning. We make sure that data processing through machine learning models respects the privacy of their training data, and that after training, the model makes robust, secure, and interpretable predictions.

What infrastructure, programs and tools are used in your group? We rely on high performance computing infrastructure and GPU clusters from the Helmholtz association. Our software is mainly developed in Python and deployed to the GPU clusters in form of GitHub containers.

What could a guest researcher learn in your group? How could he or she support you in your group? The participant will learn how to automatically process data and manage information through the lens of trustworthy machine learning. They will be given the opportunity to discover the risks to machine learning privacy, robustness, and security that arise from the data itself or the training procedure. They will explore mitigation methods to the most crucial risks, and get the chance to implement those themselves, or expand them to new contexts. Finally, the participant can extend their knowledge on publishing academic results in the area of trustworthy machine learning, and will, in the best case, contribute to a paper submission to a top tier machine learning conference. We welcome all participants with a background in machine learning, and an interest in developing data processing that goes beyond considering only utility of the resulting application.

Ansprechpartner

Jülich Research Centre (FZJ) - Pedestrian Dynamics – Empiricism at the Institute for Advanced Simulation: Civil Safety Research (IAS-7)

Short summary of your group's research: Our institute studies the dynamics of pedestrians to understand their behavior and thereupon develop reliable models for enhancing the safety of people inside crowds. Controlled experiments allow the quantitative description of the dynamics and enable the analysis of selected parameters under well-defined constant conditions. Data of these experiments is collected and analyzed by appropriately selected sensors and self-developed software.

What infrastructure, programs and tools are used in your group? During our experiments we are collecting a huge amount of data from (depth) cameras, motion capturing systems, IMU sensors, pressure sensors, heart rate sensors and electrodermal activity sensors sometimes combined with questionnaire data.
Our software is written in C++ or Python like our open-source tool PeTrack, which automatically extracts accurate pedestrian trajectories from videos.
Gathered data of all our experiments can be found in our open data archive.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? We are a multidisciplinary young group of physicists, mathematicians, engineers, sociologists, psychologists, and computer scientists.
Our institute is one of the leading institutions in the field of experimental pedestrian research.

The dynamics and behavior of people in a crowd have many influences. The diversity of performed experiments and the amount of experimental data in our group is large. We learned in the past that a new view, method or discipline can give us another or extended insight to our data so that we would be happy to welcome you to our group.

Ansprechpartner

Helmholtz Zentrum Hereon - Climate Service Center Germany

Three-sentence summary of your group's research: The interdisciplinary team at GERICS develops scientifically based prototype products and services to support decision-makers in politics, business and public administration in adapting to climate change. We are located in the historic "Chilehaus" in the centre of Hamburg. Specifically, my group develops applications in the field of water resources (water supply, flooding), as well as socio-economic impacts, such as damages and health impacts.

What infrastructure, programs and tools are used in your group? We operate our own regional climate model REMO, and participate in the WCRP EURO-CORDEX network. In addition, we develop state of the art tools for pre- and post-processing of climate and other environmental data.

What could a guest researcher learn in your group? How could he or she support you in your group? We use data science methods and machine learning for improving simulations as well as visualistion of our results. Example applications are the application of ML for data post-processing of weather and climate predictions (Link), impact models for flood damage assessment (Link), and health impact modelling (Link). We are also open to accept novel ideas and proposals of other applications of data science methods, that fit the goals of GERICS and our product portfolio.

Ansprechpartner

GFZ Helmholtz Centre for Geosciences - Section 1.4 Remote Sensing and Geoinformatics

Three-sentence summary of your group's research: We link fine scale observations of vegetation to global satellite observations in order to calibrate and validate EO products. For this we use proximal remote sensing like Terrestrial and UAV laser scanning, radiative transfer models and machine learning.

What infrastructure, programs and tools are used in your group? UAV and terrestrial laser scanning, hyperspectral imaging.

What could a guest researcher learn in your group? How could he or she support you in your group? A researcher can learn to design field experiments that aim to link local to global observations of vegetation. A researcher can support the group with novel ideas on using AI and machine learning, in particular to apply on point cloud data.

Ansprechpartner

CISPA - Helmholtz Center for Information Security - Relational Machine Learning

Three-sentence summary of your group's research: Our main goal is to reduce the computational costs associated with deep learning based on algorithmic innovations and theoretical insights with practical implications. We particularly enjoy tackling problems that involve graph based data. Most of the applications that motivate us are in the biomedical domain or economics ranging from modeling gene regulatory dynamics, gaining insights into cancer drivers, understanding sex differences, or analyzing systemic risk in international food trade.

What infrastructure, programs and tools are used in your group? As a lot of our projects involve deep learning and neural network sparsification, we make use of GPU clusters.

What could a guest researcher learn in your group? How could he or she support you in your group? We would be happy to collaborate on interesting applications as well as method development in the machine learning domain.

Ansprechpartner

Helmholtz-Zentrum Dresden-Rossendorf (HZDR) - Center for Advanced Systems Understanding (CASUS)

Short summary of your group's research: The Center for Advanced Systems Understanding is a new center working in the field of data-driven systems science. We strive to understand complex systems in an interdisciplinary way using newest digital technologies. From modelling the formation of complete organisms from a single cell to studying the interplay between ecosystems and biodiversity, from understanding what exoplanets look like to working on the mobility of the future, CASUS provides a diverse and welcoming research culture.

What infrastructure, programs and tools are used in your group? We provide access to a large HPC systems for both high performance compute simulations and large-scale AI. We have expertise in high performance computing, scalable AI, human-machine interaction, and strive for the development of professional research software and solutions. We have access to some of the largest compute resources on the planet.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? Participants have a large variety of options, from learning High Performance Computing (including GPU and FPGA computing) to newest developments in AI (physics-informed NN, Invertible NNs, Normalizing Flows, ...), from applied mathematics and data science foundations to using their skills for a variety of applications in physics, ecology, digital health, autonomous systems, cyber security, earth systems science and more.

Ansprechpartner

German Center for Neurodegenerative Diseases (DZNE) - Systems Medicine

Short summary of your group's research: Systems medicine at the DZNE aims to develop holistic approaches to tackle diseases such as Alzheimer’s, Parkinson’s, or amyotrophic lateral sclerosis (ALS) and translate new insights into the clinics. We aim to change how flow cytometry data is currently analyzed. Technological advances in flow cytometry increased the possible number of features to be comparable to mass cytometry. However, the most widely used analysis methods for flow data is the targeted gating approach, which relies on manual selection of cell populations. This approach has several shortcomings as results vary across experts and for one person over time, does not scale well for hundreds of patients and samples, and tends to focus on known cell types, such that new discoveries especially in the disease context become unlikely.    

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? A guest researcher can learn how to leverage deep learning models to jointly integrate flow cytometry data from different sites and enable joint integration without sharing data with our swarm learning approach.

Ansprechpartner

German Aerospace Center (DLR) - EO Data Science

Three-sentence summary of your group's research: The Helmholtz AI consultant team @ DLR provides expertise from Earth observation, robotics and computer vision and an HPC/HPDA support unit.
The team covers a comprehensive package of AI-related services. It is supposed to support both projects within LRV and cooperative projects with other Helmholtz centers. On the other hand, the EO Data Science Department develop AI methods for Earth observation data to tackle societal grand challenges.

What infrastructure, programs and tools are used in your group? Coding is mainly done in Python and C/C++, using the most popular optimization and ML/DL frameworks. We have access to GPU servers.

What could a guest researcher learn in your group? How could he or she support you in your group? We offer a broad range of AI expertise, with a strong focus on Earth observation applications. We are looking for people with knowledge in machine learning, optimization and/or image processing to apply their skills to tackle societal grand challenges using Earth observation data.

Ansprechpartner

Jülich Research Centre (FZJ) - Jülich Supercomputing Centre (JSC)

Short summary of your group's research: The joint research group ''High Productivity Data Processing (HPDP)'' at JSC and University of Iceland is highly active in developing parallel and scalable machine (deep) learning algorithms for remote sensing data processing and many other types of applications (i.e., medical research and retail sectors).  The main backbone of the research group is the large number of PhD students that are jointly supervised with the University of Iceland. Furthermore, the HPDP works actively with the Cross-Sectional Team Deep Learning (CST DL) and the Helmholtz AI consultant team at JSC.

What infrastructure, programs and tools are used in your group? By being located at JSC, HPDP can rely on HPC technologies with MPI, OpenMP and CUDA (with TensorFlow, Keras, pyTorch, Chainer, Horovod) but also on innovative quantum computing systems.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? Learn how data intensive computing approaches have become indispensable tools to deal with the challenges posed by applications from a diverse range of applications.

Ansprechpartner

Karlsruhe Institute of Technology (KIT) - Satellite Climatology

Short summary of your group's research: We focus on the development, validation and application of geophysical remote sensing techniques, with special attention to the Earth surface - atmosphere interface. Topics of special attention include cloud processes, air pollution, land-surface temperature and interactions between the land surface and lower atmosphere. Our methods are primarily remote sensing and machine learning.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? Collaboration is at the core of our approach to research. We can offer the opportunity to learn in this exchange about our topics and methods. KIT is one of the most exciting locations for atmospheric research, with world-class infrastructures, several seminar series and numerous project links to other institutions around the world. You will become part of this environment.