Helmholtz Hosts L-N

Contacts

Jülich Research Centre (FZJ)- Stratosphere (ICE-4)

Short summary of your group's research: Our main focus is on flying sensors on balloons to stratospheric altitudes up to 35 km. For instance, we collect air samples and then subsequently analyse these for their content of trace gases important for stratospheric ozone (such as CFCs or halons) or climate (e.g., CO2 or HFCs). We can use the data to better understand the state of the ozone layer or circulation changes at altitudes well above the reach of aircraft.

What infrastructure, programs and tools are used in your group? Observation platforms are aircraft and balloons, measurements are mostly based on cavity-ringdown spectroscopy and mass spectrometry. Current project examples are the ERC Starting Grant EXC3ITE and we are also involved in Modular Observation Solutions for Earth Systems MOSES. We closely collaborate with the atmospheric modelling community, e.g. the Chemical Lagrangian Model of the Stratosphere CLaMS.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? The learning experience would include the challenges of observations in as hostile an environment as the stratosphere, measurement and data retrieval techniques, and inferring key parameters such as average stratospheric transit times from the various trace gas data. Especially the latter part is where a guest researcher might be able to help us to improve our algorithms and procedures.

Contacts

Karlsruhe Institute of Technology (KIT) - Systems, Data, Simulation & Energy (SYDSEN)

Three-sentence summary of your group's research: The research group Systems, Data, Simulation & Energy (SYDSEN) is concerned with the advancement of the field of Modeling and Simulation by developing new methods to utilize nowadays prevalently available data from easily accessible Internet of Things (IoT) devices. In addition, the research group looks at the synergies between Artificial Intelligence and Simulation to enhance both areas.
The application areas focus on cyber-physical systems, including smart factories and energy systems, and enhancing various performance metrics, such as energy efficiency, production and reliability.

What could a guest researcher learn in your group? How could he or she support you in your group? How to use data-driven methods to enhance modelling and simulation processes.
The guest researcher can participate with collaboration in ongoing or new research, as well as knowledge dissemination and participation in classes.

Contacts

Helmholtz Centre for Environmental Research (UFZ) - BioGeoOmics

Short Summary of your Groups research: The research group BioGeoOmics integrates state-of-the art instrumental and methodological approaches to study dynamic interactions of molecules in the environment. These molecular interactions include natural processes (e.g. metabolism, organic matter diagenesis) as well as anthropogenic interferences (interaction of biological systems with chemicals). In addition to analytical method development we also develop chemoinformatics and machine learning aided data evaluation techniques.

What infrastructure, programs and tools are used in your group?At the core of the the analytical toolkit of the BioGeoOmics-group, we use ultra-high resolution mass spectrometry (FT-ICR MS) and mass spectrometric imaging (MSI). We devolp methods for non-target  metabolomics  and biogeochemistry using stable isotopes to study chemical and biological transformations of organic molecules, their spatial localization in tissues, and to identify metabolites and unknown chemicals and transformation products in complex mixtures.
Our group develops (automated and interactive) data processing pipelines using R, the ETL platform KNIME, and PostgreSQL DBMS. We apply multivariate statistical tools and machine learning models to extract knowledge from the zentillion data points generated from ultra-high resolution mass spectrometry and large evvironmental data sets.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? Since our group has a focus on non-targted mass spectrometry, our guest researchers will be able to learn how to utilize and interpret non-targeted data, how to use statistical tools to evaluate the data quality, and how to derive confidence limits inorder to make sound statements about the data. Besides instrumental training on state-of-the art FT-ICR MS systems, we can provide helpful insights about data management and quality control.
Future guest researchers are welcome to provide input on fast and efficient visualization strategies for large amount of data points, on (technical legal) aspects) of software publication, and on continuous integration/deployment.

Contacts

German Aerospace Center (DLR) - Autonomy and Teleoperation

Short summary of your group's research: The Institute of Robotics and Mechatronics, Department of Autonomy and Teleoperation explores all aspects of robot autonomy. This includes autonomous operation with AI-based approaches to task and motion planning, as well as teleoperation modalities for remote operation under human supervision with intuitive interfaces. All of this is embedded in fault-tolerant autonomy architectures that enable resilient decision making, inspection, monitoring, and error handling. The design of software components for embedded and distributed computing platforms enables the use of these methods for space applications under hard real-time conditions.

What infrastructure, programs and tools are used in your group? To increase the autonomy of robotic systems we investigate techniques to generate robot programs from logical task descriptions (e.g. PDDL). The robot Rollin Justin (see picture) is one of several demonstration platforms for these tasks. The software stack of the robot is based on different modules developed in Python, C++, and Simulink.  We develop model-based techniques for decision making leveraging real-world robot telemetry as well as semantic information inferred through physics-simulations (e.g. Gazebo). To control the robot (e.g. from aboard the ISS) intuitive control modalities are developed such as shared control and supervised autonomy.

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 visiting researchers the opportunity to participate in the development of software for some of the most advanced robots in the world. We utilize advanced programming techniques for use under real-time and non-real-time conditions. This includes integrating complex control strategies for compliant robots with AI-based reasoning methods, developing physics-based reasoning for error handling, and developing multi-modal robot interfaces in the context of astronaut-robot collaboration. Visiting researchers will learn to apply an agile, concurrent software engineering process to develop software components for space-qualified robots.

Contacts

Jülich Research Centre (FZJ) - PET Physics Group, Medical Imaging Physics - Institute of Neurosciences and Medicine (INM-4)

Short summary of your group's research: The PET physics group focusses on

• PET methodology development (image reconstruction and data corrections as scatter correction and attenuation correction)
• Multimodal Imaging (Applications and Methodology for combined PET/MR and PET/MR/EEG acquisitions) System development of an UHF MR compatible BrainPET insert
• Quantitative Methods for PET and PET/MR imaging

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

• 3T MR-PET insert,  consisting of a high resolution BrainPET, (a prototype by Siemens), and a commercial 3T MAGNETOM Trio MRI scanner with several phantoms
• Access to the Radiochemistry department for generation of Radio-tracer and Radionuclides
• Access to High Performance Computing Installations
• Access to PET and MR patient and volunteer data from other studies (neuroreceptor/neurotransmitter studies, neuroonkology)
• Monte Carlo Simulation tools Geant4/Gate, analysis tools: PMOD, SPM

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? Our group offers the following expertises

• Thorough understanding of PET systems and Image-formation from gamma photon detection to image reconstruction including all relevant corrections (scatter, attenuation, randoms, dead time)
• Unique access to a dedicated 3T MR BrainPET insert and a 7T human MRT in a research setting with all technical an league requirements to run studies involving human volunteers
• Starting in 2023: access to a in-house developed  7T MR BrainPET insert for humans
• We would highly benefit from guest researcher with expertises in Image post processing (PET and MR), Data science for information extraction from multi parametric and multimodal images, Monte Carlo Simulations

Contacts

Jülich Research Centre (FZJ) - Simulation and Data Laboratory "Highly Scalable Fluids & Solids Engineering" (SDL FSE),

Short summary of your group's research: The SDL FSE's research focuses, amongst others, on lattice-Boltzmann methods, artificial intelligence, high-performance computing, heterogeneous computing on modular supercomputing architectures, high-scaling meshing methods, efficient multi-physics coupling strategies, and bio-fluidmechanical analyses of respiratory diseases. Furthermore, the SDL FSE aims at supporting users from engineering sciences who have already developed parallel codes but need support for the use of massively parallel systems regarding high scalability, memory optimization, programming of hierarchic computer architectures, and performance optimization on compute nodes.  

What infrastructure, programs and tools are used in your group? The group mainly uses the high-performance computing (HPC) systems available at JSC for its various simulation applications. As a simulation code, a massively parallel multi-physics framework, jointly developed with the Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University, is employed. The big data outputs generated by the simulation tools are post-processed, e.g., by in-house developed tools, ParaView, machine-learning algorithms, or by Jupyter-based scripts.        

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? The doctoral researcher will have access to the latest supercomputing hardware installed at JSC and will learn from the experts in the group to develop, adapt, and optimize simulation codes. He or she will support the member of the SDL FSE in running large-scale simulations on the production HPC machines at JSC and post-process the data to gain new insights to physical phenomena in the realm of fluid mechanics. Furthermore, the doctoral researcher will be able to learn how research in a European Center of Excellence in Exascale Computing is performed and has the opportunity to contribute to the corresponding cutting-edge research in bringing AI technologies along various use-cases to exascale.

Contacts

Jülich Research Centre (FZJ) - Institute of Neuroscience and Medicine, Medical Imaging Physics (INM-4): Brain Tumor Group

Short summary of your group's research:  The research of the Brain Tumor Group within the Institute of Neuroscience and Medicine focuses on multimodal imaging in patients with brain tumors, with particular emphasis on amino acid PET in combination with advanced high-field and ultra-high-field MRI. In addition to the development and evaluation of novel PET tracers and PET/MRI methods, the potential of advanced image analysis such as radiomics and deep learning for patients with brain tumors is explored. A major goal of our research is the correlation of imaging findings and radiomics with local neuropathology and histomolecular markers, for which the close collaboration with the surrounding university hospitals in Aachen, Bonn, Cologne, Düsseldorf, and others, is extremely valuable and furthermore offers a high potential for verification and translation of research results into the clinic.

What infrastructure, programs and tools are used in your group? Amino acid PET, advanced MRI, ultra-high-field MRI, hybrid MR/PET, Python, Pyradiomics, PyTorch, LifeX, PMOD, FSL, HDBET, HDGLIO, ITK-SNAP, OsiriX, Matlab, High performance computing, etc.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? 

• Amino acid PET in combination with advanced, ultra-high-field MRI in patients with brain tumors
• Advanced image analysis including static and dynamic amino acid PET and MRI
• Feature-based as well as deep learning-based radiomics based on PET and MRI in patients with brain tumors
• Correlation of PET/MRI imaging findings and radiomics with local neuropathology and histomolecular markers
• Establishment of an imaging database of amino acid PET imaging in patients with brain tumors for advanced image analysis

Contacts

German Cancer Research Center (DKFZ) - Division of Medical Image Computing

Short summary of your group's research: The Division of Medical Image Computing (MIC) pioneers research in machine learning and information processing in the context of image data analytics. We pursue cutting-edge developments at the core of computer science, with applications in but also beyond medicine. We are particularly interested in techniques for semantic segmentation and object detection as well as in unsupervised learning and probabilistic modelling.

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

• Python Software Framework / Pytorch / Phabricator
• A GPU cluster at DKFZ tailored to specific needs, ranging from nodes with RTX 2080ti all the way up to Nvidia’s DGX systems
• The DKFZ has an Openstack cluster in which you can instantiate virtual machines for CPU heavy workloads.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? At MIC we pursue cutting-edge developments at the core of computer science, with applications in but also beyond medicine. We believe a sophisticated research software system and infrastructure are key for methodological excellence, for example to facilitate highly scalable data analysis in a federated setting. In cooperation with numerous (clinical) partners, we work on the direct translation of the latest machine learning advances into relevant clinical applications. Depending on the specific interests of the participant of the HIDA training exchange many interesting and challenging aspects could be addressed. Please find a list of ongoing research project in the department on our website in the "research" section.

Contacts

Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) - Metzger Lab

Short summary of your group's research: We are an interdisciplinary group focusing on a quantitative understanding of human neurodevelopment using stem-cell derived organoids. Using machine learning to analyze the difference between normal and pathological processes, we aim to dissect the mechanisms of neurodevelopmental and neurodegenerative diseases.           

What infrastructure, programs and tools are used in your group? Python, R, 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? As an interdisciplinary group, we apply a range of computational tools to the  analysis of biological data, including imaging and genomics data.  Participants can learn data analysis techniques and provide support in developing new data science pipelines, such as machine learning applications.

Contacts

Karlsruhe Institute of Technology (KIT) - Data Analytics, Access and Applications

Short summary of your group's research: The department Data Analytics, Access and Applications (D3A) at the Steinbuch Centre for Computing (SCC) performs research in applied Artificial Intelligence engineering, contributes to the European Open Science Cloud, has in-depth expertise in federated authentication and authorization infrastructures, and works on joint research of computer scientists and researchers from the field climate and environment. Recently, we launched a team working on the prospering field of Quantum Computing with a focus on hybrid quantum-classical machine learning algorithms. 

What infrastructure, programs and tools are used in your group? Depending on the topic we use various programming languages and computing environments such as high performance computing clusters. For many challenges Python turned out to be the first choice.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? As a candidate you will be part of our lively quantum computing team where you will gain or deepen your understanding on different aspects of quantum machine learning, our current research as well as algorithmic methods. You can further learn state-of-the-art software development methods by supporting us in extending and developing scientific open source software for quantum computing.
You can complement our interdisciplinary team by generalizing and evaluating our methods based on your own background and data and thereby enhance existing activities or even kickstart new activities.
Depending on your interests you can get an overview on other recent research topics in D3A and other research departments at SCC.

At present, Jörg Meyer can only accept applications from Helmholtz-internal candidates. 

Contacts

GFZ Helmholtz Centre for Geosciences - Remote Sensing for Geohazards

Three-sentence summary of your group's research: My research interests focus on the use of radar remote sensing data to investigate processes related to various types of geological phenomena and engineering applications, such as active tectonics, landslides, floods, groundwater extraction/injection, underground mining, glacier motion and ice mass change, dam stability, and anthropogenic activities in urban areas.

What could a guest researcher learn in your group? How could he or she support you in your group? Integration of SAR data with machine learning approaches to address natural hazards

Contacts

Helmholtz Center for Information Security (CISPA) -  Rational Intelligence Lab

Three-sentence summary of your group's research:
The group’s research aims at understanding the principles that enable autonomous agents to learn from past experience and interact successfully with complex environments, and to use this understanding to design new machine learning algorithms.

What could a guest researcher learn in your group? How could he or she support you in your group?
Conducting and collaborating on world-class research projects that lie at the intersection of machine learning and economics.

Contacts

Helmholtz Munich - Biomedical Statistics and Data Science

Three-sentence summary of your group's research: We do research in high-dimensional statistics, (non-)convex optimization, network inference, causal inference, and compositional data analysis with a special interest in microbiome research and microbial ecosystems. We care about sound statistical methodology and good software that is useful for answering broad statistical questions in computational biology and microbial ecology.

What infrastructure, programs and tools are used in your group? We use R, Python, (and MATLAB) for software development, and use  GitHub for software deployment and versioning. Group communication is decentralized via Slack and Mattermost. We have (shared) office spaces at LMU Munich (City center) and Helmholtz Munich (in the North of Munich).

What could a guest researcher learn in your group? How could he or she support you in your group? High-dimensional statistics concepts, data analysis workflows for microbiome and microbial data, journal club covering state-of-the-art concepts ranging from experimental high-throughput biology to deep learning.

Contacts

GEOMAR Helmholtz Centre for Ocean Research Kiel - Marine Geosystems

Short summary of your group's research: The Oceanic Machine Vision Group at the GEOMAR works on the topic of optical underwater surveys employing artificial intelligence (AI) and classical computer vision approaches. To this end, we seek to enable cameras to serve as faithful measurement instruments and navigation sensors in the deep sea. The latter, visually challenging environment, presents us with a lot of geometric (refraction) and radiometric problems (attenuation, scattering) which we seek to solve.

What infrastructure, programs and tools are used in your group? We normally devise novel approaches writing Python / C++ Code on Linux machines storing it on GIT, potentially in some remote (over ssh) work. If desired, access to GPUs can be provided in addition.

The subsequent evaluation is often conducted on synthetic as well as real data. To this end, we maintain multiple sets of underwater imagery, specifically tailored to the problems we work on. We also have camera-light systems, which can be operated manually, inside a test tank or directly in the Baltic Sea. In addition, they can be attached to AUVs and the like, to capture actual deep sea datasets. Hence, new data can be taken, if necessary.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? The guest can learn about synthesizing visual data or taking real data in controlled conditions in a test tank, or at the actual sea. Furthermore, we can provide deep insights about geometric and radiometric problems typically encountered in underwater scenarios. We have a wide range of topics inter alia covering refraction, color correction, medium estimation, light pose optimization, structure from motion, state estimation and image segmentation/classification. We will approach those problems with the means of classical computer vision but also with neural networks in conjunction with differentiable physical models. Finally, we would be happy if the guest researcher would co-author a research paper and/or contribute to our software.

Contacts

Helmholtz Centre for Environmental Research (UFZ) - Microbial Data Science at the department of Environmental Microbiology

Short summary of your group's research: Our group strives to assess environmental health of terrestrials and man made environments by predicting how resilient/stable microbial communities are to disturbances. A special emphasis is put on the development of concepts and theories to scale microbial interactions to the real diversity found in nature. The key research topics  of the Microbial Data Science group are based on genetic potential of microbial communities, multi-omics integration and  predictive biology. Currently these topics cover:

1. Use of (in silico) mock microbial communities to test microbial ecology theories;
2. From microbial 'Big Data' to novel ecological concepts and theories;
3. Predictive analytics in microbiology, microbial ecology and environmental microbiology.          

What infrastructure, programs and tools are used in your group? We use the High-Performance Computing (HPC) Cluster EVE as our main computational resource. We develop and use tools to analyze, resolve and interpret multi-omics data, specially metagenomes. Eg.: tools for the recovery of multi-domain genomes (prokaryotes, viruses and eukaryotes) from metagenomic data. As programming languages, we mostly use Python, R, Bash and Perl to handle and process microbial big data.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? A participant from the HIDA Mobility Program will learn how to download, process and interpret metagenomic data using both publicly available datasets as well as in-house generated data. Further, the participant will work with hands on experimental design and predictive analytics using omics data. We welcome participants from a broad range of fields (e.g. microbiology, ecology, computer/data science) that are eager to learn and/or expand their knowledge in computational biology/data science.