Helmholtz Hosts P-S

Ansprechpartner

German Aerospace Center (DLR) - Institute of Flight Guidance - Department of Human Factors

Research gate 

Three-sentence summary of your group's research: The Human Factors department supports the development of advanced concepts and assistance systems for pilots, air traffic controllers and control center personnel in terms of human-centered automation. It is cross-sectionally manifested in the Institute of Flight Guidance and supports the specialist departments in the early concept phases. An interdisciplinary team of engineers, psychologists and computer scientists plans and manages validations with operators in real-time simulation, field trials and flight tests. In general, it is important to take equal account of improving the individual human-machine cooperation and the cooperation between the on-board and ground-based systems and the people involved in them.

What infrastructure, programs and tools are used in your group? - Virtual environments, High-fidelity simulators of Air Traffic Control working positions, aircraft cockpits, VR-glasses
- Physiological measurement tools like EEG, fNIRS, EKG
- Eye tracking
- Speech recognition

What could a guest researcher learn in your group? How could he or she support you in your group? - A participant could learn about data representing human factors in automated systems, as well as how data are collected in controlled experimental designs and used for informed decisions about system design.
- A participant can furthermore learn about research topics on human-machine interaction and cooperation in the field of aviation.
- Partly automated data processing/data analysis like detection of patterns and anomalies in heterogenous data (e.g. by AI)
-  Development of analysis and visualization concepts

Ansprechpartner

Helmholtz Centre for Environmental Research (UFZ) - Remote Sensing Centre for Earth System Research

Short summary of your group's research: The Department of Remote Sensing conducts innovative research to advance the understanding of the Earth system via various remote-sensing techniques. It has extensive research experience in quantifying land surface dynamics from multi-source Earth observations across scales. Another focus lies on the investigation of land-atmosphere interaction and climate extremes using novel remote sensing products. 

What infrastructure, programs and tools are used in your group? Our team has established data lake to facilitate access to and analyse of datasets from satellite, airborne remote sensing and earth system model outputs. Modelling frameworks including radiative transfer model and land surface model have also been developed in last years. Computational infrastructures including in-house HPC and external cloud computing system can also be used by guest researchers. 

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 researcher can benefit from our expertise on:
1.Quantification of land surface parameters using multi-source remote sensing observations.
2. Integrate remote sensing data and land surface modelling framework to better quantify water cycle and vegetation dynamics 
3. Explore land-atmosphere feedbacks and hydro-climatic extremes through novel use of remote sensing datasets (e.g., detection of extreme events, impacts of extremes on water cycle and ecosystems). 

The guest researcher are encouraged to contribute to the above mentioned research topics using machine learning approaches. 

Ansprechpartner

Jülich Research Centre (FZJ) - The Institute of Energy and Climate Research - Nuclear Waste Management (IEK-6)

The institute performs cutting-edge research in the fields of nuclear waste management and safety, considering the process of “Energiewende”, the transition of the German energy system. This comprises fundamental as well as applied research and development for the safe management of nuclear wastes, covering issues from the atomic scale to the macroscopic scale of actual waste forms and waste packages or the engineered barrier system of deep geological repositories for nuclear wastes. 

Short summary of your group's research: We apply innovative experimental and computational approaches across the scale to analyze and interpret complex coupled thermal-hydraulical-mechanical-chemical (THMC) processes in porous media related to energy applications.  Recently, I have  acquired an ERC project to investigate the gas water mineral interactions in confinement https://cordis.europa.eu/project/id/101040341. In this project, AI and Neural network tools will be used to (i) automate the analysis of microfluidics experiments and process experimental results (ii) derivation of  constitutive equations to rationalize complex processes and  (iii) accelerate geochemical processes.

What infrastructure, programs and tools are used in your group? Reactive transport tool software (Pflotran, Openegeosys-Gems, comsol multiphysics). Geochemical simulation. Image processing based on machine learning. In addition, we also have a  microfluidic laboratory equipped with confocal Raman microscopy, IR and fluorescence microscopy.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? Multiphase flow using Comsol multiphysics and geochemical modelling using phreeqc or GEMS. We have conducted novel experiments in the field of geochemistry; a lab on a chip approach to investigate radionuclide incorporation in mineral phases in a confined nano volume of (1-10nL) solution. The guest researcher would work on simulating droplet generation using comsol multiphysics and conduct geochemical modelling.

Ansprechpartner

Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research - Shelf Sea Systems Ecology

Short summary of your group's research: In the working group we work on analytical techniques for the determination of micro- and nanoplastics using chemometric approaches. Data analysis is mainly performed on hyperspectral FTIR and Raman images which are targeted by various chemometric approaches. In general, these approaches are linked to the environment by research cruises, projects and meta analyses.

What infrastructure, programs and tools are used in your group? We have several spectroscopic imaging instruments (FTIR / Raman) available, a selection of dedicated computers designed for data analysis. We are currently using Python and CUDA for 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? Our group can provide a large material database, instruments and calculation power for the development of chemometric approaches. We can support the guest researcher in the development of their chemometric based analysis ideas and look forward to link these with our existing or planned approaches in form of for example future research proposals with the guest researcher.

Ansprechpartner

German Aerospace Center (DLR) - Machine Learning for Planetary in-situ Spectroscopic data

Short summary of your group's research: In the DLR junior research group “Machine learning for planetary in-situ spectroscopic data”, we investigate data measured in the laboratory but also on Mars by instruments like ChemCam (NASA’s Mars Science Laboratory). We train and evaluate supervised algorithms for the prediction of elemental abundances in rocks and soils, and for the classification of mineralogical classes. Additionally, we use unsupervised methods for pattern recognition in large spectroscopic datasets and aim also for physics informed machine learning based simulations of spectra from geological targets.

What infrastructure, programs and tools are used in your group? For the machine learning part of our research, we work with frameworks like PyTorch, Sklearn etc provided in python. We have laboratory instruments that can be used to measure spectroscopic data, also in simulated martian and vacuum atmospheric conditions. The spectroscopic techniques we are mainly working with are laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. 

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? You can learn several aspects in our group starting with the basics of the spectroscopic methods and the nature of the data, especially with regard to planetary in-situ exploration. Regarding machine learning, you can learn how to optimize models trained with spectroscopic data for different purposes, such as regression and classification. Furthermore, you can benefit from our experience in real planetary mission involvements. We are looking forward to learning from your machine learning experience for other applications and how this could be adapted to spectroscopic data. In particular, we are interested in explainability of machine learning models in order to better understand the predictions and performance of them and in ways to include physical knowledge in the training process.

Ansprechpartner

Helmholtz Munich - AIDOS Lab, Insitute of AI for Health

Short summary of your group's research: Our primary research interests are situated at the intersection of geometrical deep learning, topological machine learning, and representation learning. We want to make use of geometrical and topological information—also known as manifold learning—to imbue neural networks with more information in their respective tasks, leading to better and more robust outcomes. Following the dictum ‘theory without practice is empty,’ we also develop methods to address current challenges in biomedicine or healthcare applications.

What infrastructure, programs and tools are used in your group? HPC CPU/GPU cluster using Slurm, machine learning frameworks (mostly pytorch) and data analysis tools (scikit-learn, pandas, numpy) based on Python. On the theory side, we are world leaders in topological machine learning, as nascent branch of machine learning.

What could a participant of a HIDA Mobility Program learn in your group? How could he or she support you in your group? Getting to know cutting-edge AI research in geometrical deep learning or topological machine learning, including—but certainly not limited to—graph representation learning. At HMGU, we are sitting on a treasure trove of complex high-dimensional data sets, bearing the promise to substantially advance our understanding of disease-driving mechanisms, for instance. Visitors will benefit from being exposed to a unique combination of theory and practice, with the prospect of working on challenging, impactful applications. We need motivated doctoral researchers who can support our endeavour of developing the next generation of machine learning models to boldly tackle the challenges of today and tomorrow in healthcare!

Ansprechpartner

Karlsruhe Institute of Technology (KIT) - Algorithm Engineering

Short summary of your group's research: Efficient algorithms and data structures are the basis of all nontrivial computer applications. Algorithmics – the systematic development of efficient algorithms – is therefore crucial for transforming technological potential into applications that are important for technology, business, science, and our daily lives. At the institute of theoretical informatics, our group focuses on the "basic toolbox" of methods that are needed in many applications, e.g., sorting, hash tables, index data structures, route planning in graphs, or partitioning graphs. A particular focus is on parallel algorithms ranging from shared-memory machines to the largest supercomputers. Here, basic tools we develop include load balancing, collective communication primitives, and (hyper)graph partitioning.

Helmholtz researchers outside computer science might profit from a cooperation with our group if improved basic tools can remove performance bottlenecks in their applications.

What infrastructure, programs and tools are used in your group? C++, MPI, Intel TBB, OpenMP. Our own software libraries for sorting, (compressed) hash tables, search trees, (hyper)graph partitioning, route planning,... Various parallel computers ranging from shared-memory machines to large supercomputers (e.g. SuperMUC-NG).

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

Learn: algorithm engineering (i.e., design, analysis, implementation and experimental evaluation as a holistic process), understanding of scalability issues. Use of our techniques and software.

Support: Develop improved basic algorithmic tools with us. Engineer you application with us using our techniques, algorithms, and software libraries. A goal would be to have a prominent joint publication.

Ansprechpartner

Jülich Research Centre (FZJ) - Materials Data Science and Informatics

Three-sentence summary of your group's research: Research work at the IAS-9 is located at the intersection of data science and machine learning, research data management and semantic technology, and simulation in the field of computational materials science. In the data science department  we investigate how predictive and generative machine learning models are able to replace or accelerate computationally expensive aspects of simulations; and as a second main topic we perform data mining of and method development for microscopy images and data. In the field of materials simulations we are particularly interested in predicting the structure-property relation in the context of solid-mechanical systems. In the field of RDM we are involved in software development and design ontologies and metadata schemas together with domain scientists.

What infrastructure, programs and tools are used in your group? Probably the main "tool" used in the IAS-9 is programming. 90% of the time, we use Python for writing code, but once in a while there is also some C/C++ or Java. Half of our activities are taking place on HPC hardware.

What could a guest researcher learn in your group? How could he or she support you in your group? A guest researcher can learn how (data) science is done in our team and how we sometimes also collaboratively work together on small projects -- sometimes with people from the field of simulation, research data management, and data science. 

Ansprechpartner

Helmholtz Munich - Comparative Microbiome analysis

Three-sentence summary of your group's research: The human microbiome is a key component for our health. It is strongly influenced by environmental microbiota, which interact with the microbiome of barrier organs like skin or respiratory system. As a consequence, the reduced microbial diversity in the environment, resulting from climate- and global change, strongly impacts human – environment interactions, resulting in an increase in environmental diseases and infections. According to the planetary health concept the prevention of such diseases requires strategies which increase biodiversity in the environment.
We identify key microbiota from the environment, which trigger our health, develop strategies to promote the abundance of those microbiota in urban and indoor environments and analyze consequences for our health.

What infrastructure, programs and tools are used in your group?:  We do next generation sequencing of microbiomes using metagneomics approaches from various sources and perform high throughput bioinformatics including KI based tools to reconstruct genomes of microborganisms.

What could a guest researcher learn in your group? How could he or she support you in your group?: In silco genome assembly of microbiota from metagenomics data and functional predications. 

Ansprechpartner

Helmholtz Munich - Institute of Machine Learning in Biomedical Imaging

Short summary of your group's research: The Institute of Machine Learning in Biomedical Imaging (IML) focuses on research to leverage machine learning for the grand challenges in biomedical imaging in areas of unmet clinical need. Its goal is to fundamentally transform the use of imaging for diagnostics and prognostics. Novel and affordable solutions should empower clinics to make more accurate, fast and reliable decisions for early detection, treatment planning and improved patient outcome.

What infrastructure, programs and tools are used in your group? Novel and affordabel solutions should empower clinics to make more accurate, fast and reliablie decisions for early detection, treatment planning and improved patient outcome.

What could a participant of a HIDA Mobility Program learn in your group? How could he or she support you in your group? Deep learning and machine learning methods for intelligent imaging solutions, from imaging sensor to clinical applications

Ansprechpartner

GEOMAR Helmholtz Centre for Ocean Research Kiel - Data Science Unit

Three-sentence summary of your group's research: The Data Science Unit (DSU) of GEOMAR develops Data Science methods for marine research and applies them together with the researchers to the heterogeneous data of GEOMAR and its partner institutes. The DSU offers support and training on Data Science methods to researchers at GEOMAR. We aim to adress the grand challenges of marine science with data science methods.

What infrastructure, programs and tools are used in your group? We work with all data types relevant to the marine sciences: time series (e.g. in climate research, ecology), grids (e.g. in seafloor mapping, microscopy imagery), cubes (e.g. in ocean current simulation, 4D visualization). Our data products are developed and operated interactively (e.g. Jupyter notebooks) and most of the magic happens in Python. High-performance computing can be conducted on HGF computing ressources, at our compute cluster at Kiel University or on mobile GPU clusters for HPC computing at sea.

What could a guest researcher learn in your group? How could he or she support you in your group? Working with marine science data sets. Detecting and classifiying events in heterogeneous data (time series, grids, data cubes, ...). AI and ML methods. Supervised / unsupervised clustering of data. Integration of AI, simulation, and observation methods and data into Digital Twin frameworks. Working towards solutions for pressing ocean and society challenges.

Ansprechpartner

Helmholtz Centre for Environmental Research (UFZ) - Bio-Data Science 

Short summary of your group's research: The new research group Data Science under the supervision of Dr. Jana Schor focuses on Data Science methods, like Machine Learning and Data Integration, to boost the extraction of knowledge from human and environmental health Big Data sets. We apply and develop respective computational methods on public and in-house data sets, mainly in the field of environmental and health research, to generate novel hypotheses, and make predictions on a large scale. To increase the credibility of our AI approaches we introduce explainability and methods for the quantification of uncertainty into our applications. We further attach great importance on the principles of reproducible research.           

What infrastructure, programs and tools are used in your group? High performance computing infrastructure at the UFZ and storage as well as data integration capabilities of the European Open Science Cloud; Software is developed in R, and Python, and is provided in Singularity containers and/or in public gitlab repositories at the HZDR; for training our deep learning models or other computationally expensive calculations we use the GPUs that are integrated into our servers and the HPC system.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? The participant will learn to organize his/her data analysis or software development projects according to clear data management plans and with SOPs that allow shared and joint code development, respective documentation, and the reproducibility of research results. We offer to share our expertise in the application of machine learning approaches and respective data pre-processing strategies. Further, we are experienced in story telling with data and offer to teach respective principles to the participants. We welcome participants with a background in machine learning who are interested in the applications of their methods to our research questions, e.g. in the field of predictive toxicology. We also invite researchers with a chemistry, (eco-)toxicological or biological background who might inspire us with interesting research questions and who are willing to learn helpful data science strategies. 

Ansprechpartner

Helmholtz-Zentrum Dresden-Rossendorf (HZDR) - Artificial Intelligence

Three-sentence summary of your group's research: We are Helmholtz.AI consultants and work on a wide range of projects. Our core strengths presently are digital twins using machine learning (simulation based inference, surrogate modelling), pattern recognition and object detection in images and other modalities, anomaly detection, uncertainty quantification and prediction robustness. As a plus, we have a strong background in teaching.

What infrastructure, programs and tools are used in your group? We use HPC infrastructure locally and available across Germany. We so far, have concentrated on the use of libraries like pytorch, scikit-learn, scikit-image to which we occasionally contribute.

What could a guest researcher learn in your group? How could he or she support you in your group? In our team, a scientist could learn about how to make a machine learning model more trustworthy (with respect to stability, fairness, uncertainty, robustness and explainability). We can offer guidance on each of the topics we specialize in: digital twins, surrogate modelling, object recognition, anomaly detection, prediction robustness. We can also help to set up a reprucible and scalable machine learning workflow. If you like to learn more, please let me know.

We always welcome support in converting our learnings into open-source tools that the community at large can reuse subsequently.

Ansprechpartner

German Aerospace Center (DLR) - Clinical Aerospace Medicine (LRM)

Three-sentence summary of your group's research: Long duration human spaceflight missions create medical support challenges for eye changes, which can occur in nearly two-thirds of astronauts. To address these challenges, we are developing artificial intelligence applications to support crew members in monitoring their eyes. These applications have the potential to be used for crew medical support aboard the International Space Station, and beyond.

What infrastructure, programs and tools are used in your group? For the machine learning component of our research, Python, convolutional neural networks, Tensorflow, GPU servers, and computer vision tools are used to conduct our analyses. To collect the raw image and video data used in our analysis, we use ophthalmology imaging tools (e.g., fundoscopy, optical coherence tomography (OCT), etc.) commonly used in clinical practice worldwide. You would require access to a development environment (e.g., VSCode, Pycharm, etc.), understanding of and adherence to data security and ethics standards, and a modern smartphone/tablet.

What could a guest researcher learn in your group? How could he or she support you in your group? In our group, you could learn about the eye changes astronauts experience during long duration spaceflight, especially with regard to the retina. You could advance your machine learning skills to optimize regression, classification, and object detection models trained with our image and video data. In particular, we are interested in and open to new ways to improve the predictions and performance of our networks and models. By joining our group, you could benefit from our experience in aerospace medicine, human spaceflight, and International Space Station research, and we would benefit from your expertise in computer vision for human medical data. We would look forward to the possibility of working together.

Ansprechpartner

Jülich Research Centre (FZJ) - Earth System Data Exploration research group at the Jülich Supercomputing Centre

Short summary of your group's research: ESDE explores a wide range of machine learning methods for the analysis and forecasting of weather, climate and air quality data. We focus primarily on large, computationally intensive problems and adopt high-end deep learning approaches. Next to machine learning research we are also strongly interested in developing FAIR big data workflows. The 20+ group members have diverse educational backgrounds and build a lively international team of highly motivated researchers and software developers.

What infrastructure, programs and tools are used in your group? We make good use of JSC's high-end computing systems (https://go.fzj.de/juwels) and also operate on smaller GPU clusters. Tensorflow and Pytorch are our main (Python) software libraries. We also use containers and tools like Horovod to port ML codes to the supercomputer.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? If you already have a good background in advanced machine learning techniques (i.e. beyond MLP) you may take it to the next level with a visit to our group and we will be happy to teach you the necessary basics of weather and air pollution science so that you can apply your skills to the problems we work on in our group. If you have a strong background in environmental science but only limited experience with machine learning (but you know how to program in Python) you may learn a lot about more sophisticated deep learning methods and we might be able to integrate your specific experience to better address our environmental research questions.