Helmholtz Hosts O-S

Contacts

Jülich Research Centre (FZJ) - System Medicine, Brain and Behaviour (INM-7), Institute of Neuroscience and Medicine

Short summary of your group's research:  We use a multi-level approach utilizing computational tools and big data to gain an overall insight into neurological and psychiatric disorders to pave the way for personalized medicine. In particular, we focus on the interplay between sleep/sleep disorders and medical and mental conditions using phenotypic and multimodal neuroimaging data (e.g., sMRI, fMRI, PET). 

Masoud Tahmasian is part of Prof. Dr. Simon Eickhoff's group on System Medicine.

What infrastructure, programs and tools are used in your group? We use state-of-art methodologies such as machine-learning to big clinical and population-based databases using high-performance computing tools.

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 will apply several analyses including machine-learning to identify the role of sleep disturbances and brain structures on predicting neuropsychiatric conditions such as depression using a large-scale general-population sample (e.g., UKBB).

They should perform the analyses independently and help us to build a nice analytic framework to find the neurobiological mechanisms behind the link between sleep and neuropsychiatric disorders.

Contacts

Helmholtz Centre for Environmental Research (UFZ) - Computational Landscape Ecology - BIOECOS (BIOdiversity & ECOsystem Service)

Three-sentence summary of your group's research: My research interests are in the field of landscape ecology, with a focus on animal ecology, biodiversity monitoring, data syntheses and meta-analyses.
I am a supporter of open science and FAIR principles. I mostly R and sometimes Python to analyze published studies and big datasets. 

What could a guest researcher learn in your group? How could he or she support you in your group? Guests can participate in data synthesis projects and meta-analyses. We summarize large amounts of literature in a comprehensive way, in order to provide a complete picture of evidence to scientists, stakeholders and decision-makers. 

Contacts

Helmholtz Munich - Computational Immunobiology Lab - Institute of Computational Biology - Computational Health Department

Short summary of your group's research: Our lab specialises in translational single cell biology with focus on infectious diseases. We develop integrative Carlos Talavera-LópezAI approaches to characterise cellular behaviour in health and disease with the aim of identifying diagnostic biomarkers that can be easily deployed in the clinic.

What could a participant of a HIDA Mobility Program learn in your group? How could he or she support you in your group? We are looking for a curious, dedicated scientist interested in learning to apply AI/ML methods to single cell multiome data, and to help us find ways to better communicate biological insights using novel data visualisations. Together, we will better understand the cellular social networks of inflammatory processes, and how these interactions could be potentially be used as diagnostic biomarkers.  

Contacts

GFZ Helmholtz Centre for Geosciences - Earth Surface Process Modelling/Hazards and Surface Processes Research Group

Short summary of your group's research: Our research group studies hazards and related surface processes across a wide range of environments, from mountain regions to coastal areas and even deep oceans, over different time scales. Our work has wide topics ranging from earthquake and tsunami, storm and hurricane, landslide and debris flow, to flood and paleo-flood. We use various tools and methods, including field surveys, remote sensing, environmental seismology methods, processes-based modelling, and data science methods, including machine learning to understand the physical processes behind all these natural hazards. 

What infrastructure, programs and tools are used in your group? We use the High-Performance Computing infrastructure, including GPU and CPU clusters from our section, GFZ or Helmholtz association, as our main computation resource. For programming languages, we have used different mainstream languages, including Fortran, C, C++, Python, R, Matlab, and Julia, depending on the project. Regarding the data science model, we develop and use open-source platforms such as Scikit-learn, Tensorflow, and PyTorch.

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 and diverse research group, we can offer many opportunities for the participants with various interesting projects. There are two main types of projects: method-development-oriented projects and application-oriented projects. For a method-oriented, the participant could learn and develop physics-based machine learning methods for solving partial differential equations or investigating physics. An application-oriented participant could work on earth science or hazard-related research in the project using methods borrowing from computer vision, natural language processing, or signal processing. The participant will involve and support us in developing open-source data science model toolkits for the earth science and hazard science communities. Detailed project descriptions can be found on our group homepage. Feel free to contact us if you have any questions about the projects or bring your own ideas. We specifically welcome applicants from under-presented groups and third-world countries. 

Contacts

Jülich Research Centre (FZJ) - Modeling and Simulation @ Institute of Bio- and Geosciences 1

Short summary of your group's research: We provide our local colleagues and the scientific community with state-of-the-art models, algorithms and software. Current challenges in strain selection and optimization as well as process analysis and development are addressed in a strongly data-driven fashion in close collaboration between theory, simulation and experiment. Typical projects involve data analysis, model calibration or training, uncertainty analysis, experimental design. Model predictions are applied for testing hypotheses and focusing experimental work.             

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group?  Many projects require advanced numerical methods and high-performance computing, in particular when machine learning/ artificial intelligence, Monte Carlo simulations, global optimization or CFD simulations are applied. We develop and maintain dedicated software packages, most of which are published as open source code https://github.com/modsim.

Contacts

Jülich Research Centre (FZJ) - Ernst-Ruska-Centrum

Short summary of your group's research: We are developing solutions for high-performance data processing, data management and automation in electron microscopy. This includes software like here as well as IT systems and infrastructure. Since modern detectors for electron microscopy can reach a data rate of 50 GB/s, datasets can contain terabytes of data, and electron microscopy is a visual and interactive method, these solutions can be characterized as interactive high performance computing.

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

• MapReduce-like processing, "divide and conquer", linear algebra, inverse problem solving
• Python, NumPy, SciPy, Dask, Numba, CuPy, Torch, Jupyter, JupyterHub;
• Linux, Windows;
• Nextcloud, NAS, NFS, CIFS, SSH;
• GitHub, GitLab, Azure Pipelines, PyPI, Zenodo;
• Tox, Pytest, Sphinx;
• product management, project management, software development, test-driven development, Agile;
• PCIe 4.0, SSD RAIDs, 10 GBit Ethernet, AMD EPYC, GPGPU

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 high performance data processing with Python, in particular throughput-oriented processing of large-scale data. That can also involve finding suitable mathematical approaches and implementation strategies to transform an existing "proof of principle" reference implementation into a production-ready high-performance tool. Typically, we achieve speed-ups of 100x to 1000x from a "naive" NumPy or Matlab-based implementation.

Furthermore, you can learn software development methodologies, in particular the "GitHub flow", in combination with quality assurance through automated tests and continuous integration.

Contributions to scientific Open Source software are highly appreciated as a form of support. That can involve software which we actively maintain and develop, work on upstream dependencies, or all other forms of contributions to the Open Source software ecosystem. Such contributions can be in-kind by simply improving existing software and/or releasing new software under an Open Source license, or by joint applications for funding where parts of the resources are dedicated to software development and maintenance.

Contacts

Deutsches Elektronen-Synchrotron DESY - Scientific Computing (Photon Science)

Short summary of your group's research: We are developing computational tools for processing large crystallographic datasets consisting of many hundreds of thousands of individual frames.  Recently, we are moving towards real-time data processing, with the aim of making the data analysis step "disappear" - becoming part of the data acquisition process.  Our main product embodying this work is the free and open source "CrystFEL" software package.

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

Infrastructure: Gitlab, DESY "Maxwell" cluster

Tools: HDF5, SLURM, GSL, OpenCL, ZeroMQ, MessagePack, CMake, Meson, GTK and more

Languages: C/C++, Python, Lisp and others         

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? As well as working with large-scale accelerator-based photon facilities (PETRA III and European XFEL, plus other facilities worldwide), you would gain insight into developing a widely-used (>100 worldwide users) domain-specific software package.  For instance, how to balance the implementation of cutting-edge scientific methods with other considerations such as stability and reliability.  During your stay, you could contribute (for example) by experimenting with a new way of processing data, speeding up existing processing methods, or implementing one of the many requested features in CrystFEL.

Contacts

Jülich Research Centre (FZJ) - IBG-1: Biotechnology

Short summary of your group's research: Modeling, Simulation and Data Analytics in the fields of systems metabolic engineering and bioprocess development: Cell and process modeling, Omics and bioprocess data processing, process parameter estimation, microbial image analysis, digitalization in lab automation     

What infrastructure, programs and tools are used in your group? Hosting CADET and 13CFLUX2 software systems. Using and developing C++, python, Matlab toolboxes. High performance parameter estimation in complex systems, MCMC for parameter estimation and multi model analysis. Deep learning for image analysis. Supercomputing applications in process simulation.        

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

Wanted: Expertise in cutting edge Bayesian methods, image analysis and high performance computing

Offering: Challenging realistic problems from the above mentioned fields

Contacts

Karlsruhe Institute of Technology (KIT) - Institute of Microstructure Technology

Short summary of your group's research: My research focuses on:
- development of elastoclaoric cooling device
- shape memory alloys and elastomers for elastocaloric cooling
- solar systems (PV, solar thermal, PVT) for cooling, heating and power
- solid-state cooling/heat pump

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? Next-generation sustainable zero-carbon energy technologies for cooling, heating and/or power, with emphasis on the renewable energy sources (e.g., solar energy) and/or the waste heat, with the ultimate goal of reducing greenhouse gas emissions and mitigating the impacts of climate change for sustainable future.

Contacts

Deutsches Elektronen-Synchrotron DESY - Controlled Molecule Imaging group in Center for Free-Electron Laser Science

Short summary of your group's research: Our group is working on the development of computational techniques and high-accuracy simulations to improve our knowledge of the nuclear dynamics and spectra of molecules interacting with external electromagnetic fields. We're also working on ways to simulate ultrafast imaging experiments like photo-electron circular dichroism and laser-induced electron diffraction. We have recently begun to investigate various machine learning tools in applications to our problems, which range from solving differential equations to developing effective representations for molecular potential energy surfaces, as well as analyzing and inverting experimental photoelectron imaging data. 

What infrastructure, programs and tools are used in your group? HPC, scientific computing and machine learning tools implemented in Python and Julia.

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? You will develop or expand your grasp of many theoretical, computational, and experimental aspects of quantum molecular dynamics, high-resolution spectroscopy, photoelectron imaging, and machine learning applications to these challenges. You may learn more about software development techniques by contributing into development of open source software for molecular dynamics simulations.
We offer highly exploratory projects in development of neural networks for efficient solution of Schrödinger equation for nuclear motion and photo-electron dynamics, mathematical analysis of these approaches, and analysis of experimental photoelectron imaging data. We welcome applicants from a variety of disciplines with some background in machine learning and can offer and adapt a range of possible projects.

Contacts

Jülich Research Centre (FZJ) - Reactive Transport Modelling Group / Institute of Energy and Climate Research, IEK-6Reactive Transport Modelling Group / Institute of Energy and Climate Research, IEK-6

Short summary of your group's research: Analysing complex coupled thermo-hydraulic-mechanical-chemical-transport processes relevant to environmental systems on different scales.
My research at IEK-6 focuses on the development of complementary computational approaches to analyse and interpret the complex coupled THMC-processes in the near- and far-field of geological repositories for radioactive wastes on different scales, employing high-performance computing (HPC) resources provided by the Jülich Supercomputing Centre. This work aims at an enhanced process and system understanding across scales as well as at the reduction of uncertainties and conservatisms in performance assessments, contributing thus to the scientific basis for an in-depth comparison of different repository concepts and sites as required for the German site selection procedure.

What infrastructure, programs and tools are used in your group? We use the Lattice Boltzmann Method (LBM) for simulations. The High-Performance Computing (HPC) Cluster JURECA-DC is our main computational resource. Currently, we are using CUDA, Matlab, Python, Comsol, and Fortran for developing our numerical framework and 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? The guest researcher could learn the Lattice Boltzmann method algorithm, multiscale modeling processes, and an understanding of coupled THMC-processes in geological porous media. Use of our techniques and software.
We will support the GPU High-Performance Computing Cluster in JURECA-DC. We will cooperate with the guest to develop their LBM codes. We also welcome guests with a basic background in machine learning, who want to perform joint research with us relevant to environmental systems.

Contacts

Helmholtz-Zentrum Hereon - Department of Sediment Transport and Morphodynamics

Short summary of your group's research: Continental margins are constantly changing their shape through erosion and sediment deposition. In terms of climate protection and sustainable coastal development, the “Sediment Transport and Morphodynamics” department studies in detail the interactions between morphodynamics, ecosystem functions and biogeochemistry. We apply process-based models combined with artificial intelligence to investigate particle transport, coastline change, morphodynamics and their role in ecosystem functioning.

What infrastructure, programs and tools are used in your group? We develop and apply numerical models (physics-biology-biogeochemistry coupled) with parameterizations optimized by data-driven approaches. The models include SCHISM, ROMS and Delft3D.   

What could a participant of the HIDA Mobility Program learn in your group? How could he or she support you in your group? We provide guidance to learn hybrid modeling combining process-based models and data-driven optimization. We hope the guest research could bring the expertise in artificial intelligence with application to remote sensing data (especially satellite images) and causal linkage identification from multivariate datasets. 

Contacts

Helmholtz Center for Information Security (CISPA) - Adversarial Machine Learning 

Three-sentence summary of your group's research 

Our group focuses on tackling critical research challenges in Trustworthy AI using principled adversarial ML methods. We conduct theoretical analysis to understand the failure modes ofML algorithms, design auditing tools to characterize the worst-case model behaviors against adversarial manipulations, and leverage the insights to build useful ML algorithms toward building more reliable AI systems for various applications.

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

We conduct experiments using high-performance computing infrastructure (e.g., GPU clusters) and modern AI model APIs (e.g., OpenAI GPT models). We mainly write Python programs and leverage open-source ML frameworks (e.g., PyTorch) in our daily research.

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

Guest researchers will learn about state-of-the-art topics in trustworthy ML, conducting/collaborating on interesting research projects ranging from the theoretical or algorithmic side of adversarial ML to real-world trustworthy AI applications. We are looking for visiting PhD researchers who are insterested in trustworthy ML research or are passionate to design useful ML algorithms for application domains, such as privacy/security and scientific discovery.

Contacts

Helmholtz Center for Information Security (CISPA) -  Machine Learning and Data Privacy

Three-sentence summary of your group's research: Yang Zhang is a faculty member at CISPA Helmholtz Center for Information Security, Germany. His research concentrates on trustworthy machine learning. Moreover, he works on measuring and understanding misinformation and unsafe content like hateful memes on the Internet. Over the years, he has published multiple papers at top venues in computer science, including CCS, NDSS, Oakland, and USENIX Security. His work has received the NDSS 2019 distinguished paper award and the CCS 2022 best paper award runner-up.

What could a guest researcher learn in your group? How could he or she support you in your group? The intern will either lead or assist in a research project conducted in the group. The intern will mainly work on the empirical side of the research, like collecting data, running machine learning models, etc.

What could a guest researcher learn in your group? How could he or she support you in your group? The guest researcher could learn experiences from our research domains and learn each other.

Contacts

Helmholtz Centre for Environmental Research (UFZ) - Compound Environmental Risks

Three-sentence summary of your group's research: We work on better understanding and modelling compound weather and climate events and their impacts. We use statistical and machine learning approaches to analysis climate and impact data to better understand compounding climate drivers of impacts, We further analysie climate model projections to estimate future climate risk. 

What infrastructure, programs and tools are used in your group?  We analyse large amounts of data, learn new statistical and machine learning approaches to estimate climate risks.