Helmholtz Hosts H-K

Ansprechpartner

Max Delbrück Center for Molecular Medicine (MDC) - Computational methodologies and omic analytics

Short summary of your group's research: We are a computational biology group working with a range of single-cell omic data modalities including proteomics, transcriptomics, epigenetics and genomics to study biological systems such as development, haematopoiesis or leukaemic stem cells and their niche.Establishment of efficient computational methodologies for analysis of large single-cell omic data sets, resolution of complex lineage trees, data integration and interpretation across multiple modalities and assays as well as mathematical formulation of the applied methods are of central interest in the group.            

What infrastructure, programs and tools are used in your group? Python, R, computational tools for single-cell data analysis

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? Hight-throughtput measurements of molecular states at the single-cell level are today accessible for biological and clinical studies, thanks to new and still evolving technological developments. Located in the cool central Berlin district, we are an interdisciplinary group trained in physics, mathematics, bioinformatics and molecular biology working together to address several aspects of computational analysis of such newly emerging datasets. We are interested in extending our machine learning and data science collaborations and contacts.

Ansprechpartner

German Aerospace Center (DLR) -  Energy system analysis 

Three-sentence summary of your group's research

The Energy Meteorology Group has a long experience in solar forecasting, pv-modelling and satellite retrieval. We operate a sensor and camera network in North West Germany.

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

python and pytorch

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

You will use meteorological data, camera images, satellite images and photovoltic power measurements. You will learn, how we model PV power from these input data. You will support us in forecasting PV-power-production for the next minutes and hours form these input data.    

Ansprechpartner

GEOMAR Helmholtz Centre for Ocean Research Kiel - Dynamics of the Ocean Floor / Geodynamics

Short summary of your group's research: We apply numerous geophysical methods to understand sub-seafloor processes for tectonic, environmental and resource questions. Our expertise involves not only the evaluation of the individual methods / datasets, but has recently evolved into the application of state-of-the-art machine learning workflows to integrate disparate geophysical and geological data on various spatial scales.

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

HPC Computing Centres at CAU Kiel
Open-source programms for interpreting Electromagnetic data
Python tools for ML

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 seeking for guest researchers with various geophysical data (e.g. CSEM, Seismics and Borehole Data) seeking to develop new concepts on integrating these into a consistent subsurface model. 

Ansprechpartner

German Aerospace Center (DLR) - SAR Technology department of the Microwave and Radar Institute

Short summary of your group's research: The SAR Technology department addresses everything from building up the sensor (currently the multi-frequency fully polarimetric F-SAR), planning and executing measurement campaigns, SAR processing (i.e. focusing, geocoding, image enhancement), up to the analysis of the final image product. The Machine Learning team mainly focuses on the last part, i.e. using machine learning to derive higher level information (e.g. semantic maps, geo-/bio-physical parameters) from the SAR images. Topics of relevance are single-image superresolution, denoising (despeckling), ensemble learning, deep learning, image synthesis, semantic segmentation, semi-/weakly-/self-supervised learning, XAI, out-of-distribution/anomaly detection, etc.

What infrastructure, programs and tools are used in your group? Programming is mostly done in python or C++; deep learning developments either in PyTorch or Tensorflow. We do have access to GPU servers. 

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 insights in all aspects of SAR, including hardware, focussing, processing, and analysis, combined with a profound knowledge in machine/deep learning, and applied to interesting and highly relevant applications (e.g. forest monitoring, urban growth, development of glaciers, etc.). We are looking for people with knowledge in machine/deep learning and/or image processing to apply their skills to a very exciting and powerful type of image data, i.e. SAR, or to use machine learning and SAR to solve relevant tasks in emerging applications.

Ansprechpartner

German Cancer Research Center (DKFZ) - Division of Medical Physics in Radiation Oncology

Short summary of your group's research: The division of medical physics in radiation oncology at DKFZ comprises multiple research groups, which cover all steps of the radiation treatment chain. We apply machine learning for improving innovative radiotherapy techniques using photons and ion beams. This includes technical developments, improvement of patient-oriented workflows as well as treatment response analyses.
In particular, we focus on the following topics:
1. Image-based patient and motion modeling,
2. Mathematical Methods to optimize physical and biological treatment parameters and dose distributions,
3. Treatment Outcome Analysis & Prediction (Dosiomics & Radiomics),
4. Analysis of molecular and imaging data from preclinical biological studies.

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

• Latest generation of radiotherapy treatment machines for image-guided radiotherapy (Ethos, MRLinac, particle therapy)
• Access to data from imaging modalities: MRI, CT, PET
• IT Hardware: Workstations & Servers for AI workflows, OpenStack Computing Cloud
• IT Software: Commercial and Open-Source Treatment Planning and Image Processing Software
• Various series of seminars, lectures and courses
• Close collaboration with data scientists, medical physicists, radiation oncologists, radiologists and biologists

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

Participants of the HIDA Mobility Program  will get

• Access to strong interdisciplinary and translational work
• Insights into the latest radiotherapy and imaging developments
• In touch with applications of machine learning to medical (radiotherapy and imaging) data

Candidates could support in data analysis, development of new AI methods and bringing new ideas by translating solutions from a different research field to medical physics. 

Ansprechpartner

Helmholtz-Zentrum Hereon - Group for Modeling of Hydrogen Storage Materials at the Institute of Hydrogen Technology

Short summary of your group's research: We perform state-of-the-art multi-scale simulations of hydrogen storage materials, e.g. interstitial metal hydrides or complex hydride systems. For that, we develop digital workflows bridging the atomistic scale with the mesocopic regime by coupling accurate ab-initio calculations with thermodynamic modeling and phase-field simulations. Our set out goal is to be able to understand and accurately predict materials properties as independently from experimental input as possible.

What infrastructure, programs and tools are used in your group? We have access to Hereon's own HPC cluster (more than 160 nodes with 48 CPUs/node) that we utilize for running various molecular and periodic quantum chemical program packages (VASP, QuantumEspresso, CASTEP, ORCA, Gaussian etc.)

Furthermore, we use open-source software to perform thermodynamic modeling (OpenCALPHAD) and phase-field simulations (FiPy, MOOSE).

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

What you can learn in our group:

• You will be able to gain or deepen your understanding in quantum chemical methods like DFT or AIMD for structural optimization, calculation of reaction energies and pathways and prediction of spectroscopic properties
• You will learn how to perform thermodynamic modeling to predict phase diagrams for multi-component systems via an ab-initio assissted CALPHAD methodology
• You can be trained in utilizing phase-field simulations for prediction of microstructure evolution and kinetic properties of materials on the mesoscale

How you can support us:

• You can complement our team by working with our established methodologies on your own project on the topic of hydrogen storage materials
• You can help with automatizing and streamlining some of our scale-bridging, digital workflows
• You can kickstart our planned activities for utilizing machine-learning techniques for some of our computational methods

Ansprechpartner

German Aerospace Center (DLR) - Department Real-Time Data Processing, Sensor-AI Group

Three-sentence summary of your group's research: The new SensorAI group in the OS-EDP department will focus on challenging data science and sensing problems related to sensors and optical instruments and imaging devices. This includes the design, development and evaluation of data aggregation methods, calibration and recovery algorithms, and high-dimensional data analysis. Recent theoretical advances in sensing (compressed sensing, low-rank recovery and super-resolution) and AI methods (from deep learning to neural-augmented/unfolding algorithms, PINNs and more recent AI architectures) will be used to address inverse problems in signal processing and physics.

What could a guest researcher learn in your group? How could he or she support you in your group? 
What could a guest researcher learn in your group? How could he or she support you in your group?:
Guest researchers are invited to cooperate on topics with the SensorAI group in the EDP department. This includes (but is not limited to):
1) AI for solving inverse problems in signal processing, physics and data science. 
2) deep learning, neurally-augmented/unfolding algorithms
3) uncertainty quantification
4) Quantum computing

Ansprechpartner

Helmholtz Munich - Reliable Machine Learning

Three-sentence summary of your group's research: Our main research interests include causality, interpretable dynamical systems modeling, as well as machine learning systems that interact with humans, where we focus on reliable, fair and socially beneficial systems.

What infrastructure, programs and tools are used in your group? We make use of high performance CPU and GPU compute clusters (via Slurm) and regularly use ML frameworks (mostly jax and pytorch) together with the standard Python data analysis and visualization stack (scikit-learn, pandas, numpy, matplotlib, etc). On the theory side, we are interested in (partial) identifiability of causal effects in complex data modalities as well as in the identifiability and estimation of dynamical systems (ODEs, PDEs) from observational data.

What could a guest researcher learn in your group? How could he or she support you in your group? You can learn about the latest and greatest in causal learning and dynamic systems modeling as well as how to train both small and (very) large ML models on one of the largest GPU clusters in Europe. You can apply cutting-edge models to novel bio-medical data to push the boundaries of machine learning powered scientific discovery, drug development, and understanding of health and disease. You can experience working right at the intersection of theory and application with lots of freedom to explore either direction to whatever extent suits you.

Ansprechpartner

Jülich Research Centre (FZJ) - Numerical and Statistical Methods

Three-sentence summary of your group's research: The ATML "Numerical and Statistical Methods" develops, implements, and tests new procedures/methods in the area of stochastic ordinary and partial differential equations that arise, for example, in image processing, acoustic, electromagnetic, and elastic scattering problems, as well as in reaction-diffusion-advection equations. In this context, the group also deals with algorithms for the solution of ill-posed inverse problems and the computation of eigenvalue problems taking real-world data into account. Furthermore, the group offers software support for supercomputers.

What infrastructure, programs and tools are used in your group? We develop software and tools in Fortran, Matlab, and Python depending on the requirements of the project. 
For instance, solvers for acoustic, electromagnetic, and elastic scattering for time-harmonic waves in unbounded domains have been implemented and further used for related inverse problems. The high performance infrastructure of the Jülich Supercomputing Centre is at our disposal.

What could a guest researcher learn in your group? How could he or she support you in your group? A researcher in our group will learn how to efficiently solve partial differential equations in unbounded domains with boundary integral equations numerically. A recent development is mainly focusing on the efficient numerical calculation of interior transmission eigenvalues which can be used to visualize the interior of a given three dimensional object to uncover location, size, and shape of an inclusion for either given real-world or synthetic data which is the aim in non-destructive testing. A researcher can support our group with the development of this algorithm for HPC. Moreover, the mathematical theory still needs to be established. Finally note that a detailed description of our projects can be found at our homepage.

Ansprechpartner

Karlsruhe Institute of Technology (KIT) - Mineral Dust

Short summary of your group's research: We investigate mineral dust processes with the goal to better quantify the (dust) aerosol cycle and its impacts on climate and environment. Our focus is on dust emission, dust-cloud interactions, interactions of dust with other aerosols, as well as on land-surface properties and their changes. In our research, we apply and develop numerical models, study processes theoretically, and conduct field and laboratory experiments.

What infrastructure, programs and tools are used in your group? High-performance-computing; weather models such as ICON-ART; observational instrumentation

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? We can offer expertise with numerical modeling and working with heterogeneous data sets, such as model and observational data. We are interested in expanding our activities toward artificial intelligence and novel statistical methods for data analysis. 

Ansprechpartner

Deutsches Elektronen-Synchrotron DESY - Astroparticle physics and IceCube experiment

Short summary of your group's research:

We are performing high energy neutrino astronomy using the observatories IceCube and RNO-G, as well as as the future IceCube-Gen2. Furthermore, we are working on optical surveys such as the Zwicky Transient Facility and the UV satellite ULTRASAT. The joint exploitation of the data enables new routes in multimessenger astrophysics. A key tool to combine the multimessenger data is AMPEL, a software framework developed by my research group to analyses realtime time series.

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

AMPEL, IceCube, ZTF

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

Inference of galaxy properties and properties of astronomical transients using machine learning applications on multi band time series and catalog data. 

Ansprechpartner

Karlsruhe Institute of Technology (KIT)- UQ - Junior Research Group Uncertainty Quantification

Short summary of your group's research: We develop advanced mathematical and numerical techniques for the treatment and quantification of uncertainties in complex computational models. Our research focuses on theoretical and methodological aspects, as well as on interdisciplinary projects where theoretical sound methodologies are tailored to applications. For these tasks, we employ a variety of techniques, including tools for high-dimensional approximations and machine learning, Bayesian approaches for inverse problems and data assimilation, as well as efficient and robust sampling methods.

What infrastructure, programs and tools are used in your group? Our interdisciplinary research focus necessitates the use of a wide range of tools. For example, we implement our developed mathematical methodologies in modern programming languages, such as Python, C++, Matlab, and R, depending on the project. Moreover, we take advantage of the modern high-performance computing infrastructure at KIT for computational-intensive tasks.

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 participant, you will become part of a highly motivated team. You will be able to learn about advanced mathematical and computational methodologies for the treatment and quantification of uncertainties at the interface of computational models and data. With your expertise, we will carry out novel interdisciplinary research by applying our methodologies to your data and problems. Furthermore, your application oriented expertise could inspire new ideas at the intersection of computational models and data that may spark new collaborative research activities.

Ansprechpartner

Helmholtz Centre for Environmental Research (UFZ) - Department of Computational Hydrosystems

Three-sentence summary of your group's research: Our group works on improving our understanding of hydroclimatic drivers, anthropogenic pressures, and responses to the fate of water and nutrient cycles in the terrestrial system.  We use data analytics and mechanistic modeling approaches to analyze and disentangle the varying role of climate, landscapes, and socioeconomic drivers shaping the hydrological response of the system.  Our analysis encompasses a range of temporal scales and spatial domains varying from hourly to annual timescale in small headwater catchments to big continental-scale river basins, and our investigations cover challenging freshwater resource problems with an emphasis on unraveling hydroclimatic extremes (e.g., floods, droughts, heatwaves) under historical, contemporary and future climate conditions.     

What infrastructure, programs and tools are used in your group? We use both the local PC and High-Performance Computing (HPC) systems tailored to specific needs.  We use our in-house developed multi-scale hydrologic modeling system (www.ufz.de/mhm) and water quality models for large-scale simulations.

What could a guest researcher learn in your group? How could he or she support you in your group? Given the wide diversity of our work,  we offer learning processes on the building blocks of mechanistic models, working with large-sample and large-scale databases. We welcome and invite researchers from different disciplines (e.g. computational hydrology, water quality,  agronomy, and soil sciences) to work with us on synthesizing and documenting processes, patterns, and trends of water and nutrient flow under historical and future climate and socioeconomic conditions. 

Ansprechpartner

Deutsches Elektronen-Synchrotron DESY - Controlled Molecule Imaging (Photon Science)

Short summary of your group's research: We develop innovative methods to obtain full control over large molecules and nanoparticles. These methods and the created controlled samples are exploited in fundamental physics and chemistry studies to unravel the underlying mechanisms of chemistry and biology by watching molecules at work. This is coupled with advanced experimental control and data acquisition software, including on-the-fly data reduction, as well as theoretical and computational physics – both utilizing machine learning approaches for improved performance.

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

Have a look on our website.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? Machine learning in theoretical physics and quantum chemistry and in real-time data reduction – benefit from current expertise and improve our approaches.