Find your Host Supervisor

Researcher profiles

Scroll down and discover the profiles of potential Host Supervisors at different Helmholtz Centers that would like to welcome a HIDA Trainee for a short term research stay in their group. Get to know on which kind of projects these researchers are currently working on, how you can contribute and what you can learn from their specific research approaches.

    

Martin Frank

Karlsruhe Institute of Technology (KIT):  Computational Science and Mathematical Methods

Short summary of your group's research: The research group Computational Science and Mathematical Methods (CSMM) under the supervision of Prof. Dr. Martin Frank is an interdisciplinary research group working on different challenges in mathematical modeling.  We are unified by our interest in method-oriented mathematics, in mathematical modeling inspired by applications, and in the didactics of mathematical modeling. Our research focuses on bringing modern mathematical techniques such as modeling, simulation, optimization, inverse problems, uncertainty quantification, and machine learning/artificial intelligence into the real-world. Furthermore, we take an interest in kinetic theory, developing models, numerical methods and software implementations and combining them with the aforementioned techniques.

What infrastructure, programs and tools are used in your group? Among other things, we use the modern high performance computing infrastructure of KIT, namely bwUniCluster.  We develop software and tools in Julia, Python, C++ and Matlab, depending on the project. We employ several project specific software stacks for uncertainty quantification and medical image processing.

What could a participant of the HIDA Trainee Network learn in your group? How could he or she support you in your group? As an interdisciplinary research group, we offer a lot of possibilities for a participant of the HIDA Trainee Network and many different interesting projects to support. A mathematically oriented participant could learn interesting aspects about machine learning based numerical methods for kinetic equations. Furthermore, the group offers projects in mathematical methods for uncertainty quantification and dynamical low rank.

An application-oriented participant may find interest in the project about neural network based medical image processing or uncertainty quantification in radiation therapy.

A participant, who is interested in teaching, can learn about bringing modern and method oriented mathematics to the classroom and can work on applied mathematical projects with high school students.

We develop an open source simulation toolkit for radiation therapy planning, which also offers the possibility to engage and participate. Detailed descriptions of our projects can be found on our group homepage.

   

Pascal Friederich

Karlsruhe Institute of Technology (KIT): Artificial Intelligence for materials science (AiMat), Department of Informatics

Short summary of your group's research: The AiMat group works on the development and application of AI and machine learning (ML) methods for materials science and chemistry. Focus topics are (graph) neural networks for the prediction of molecular and materials properties, the combination of interpretable AI methods, ML-enhanced simulations and automated experiments to autonomous materials acceleration platforms, and generative models for inverse materials design.

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

Infrastructure: HPC systems as BWUniCluster and HoreKa

Programs: Python and machine learning libraries, as well as quantum chemistry simulations

What could a participant of the HIDA Trainee Network learn in your group? How could he or she support you in your group?  We welcome participants with either a background in machine learning and methods development, who are interested in applying the models to interesting scientific questions, or participants with a background in materials science/chemistry/physics, who want to learn more about machine learning and teach us about their application areas!

    

Annika Bande

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

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

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

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

   

Altuna Akalin

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

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

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

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

    

Klaus Maier-Hein

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 Trainee Network 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.

   

Gabriele Cavallaro

Forschungszentrum Jülich: Jülich Supercomputing Centre (JSC)       

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

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

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

    

Timo Dickscheid

Forschungszentrum Jülich: Institute of Neuroscience and Medicine Structural and functional organisation of the brain (INM-1) - Big Data Analytics Group (BDA)

Short summary of your group's research:  The Big Data Analytics group in Jülich develops software and methodology to build a microscopic resolution 3D model of the human brain, which includes detailed information about distributions of neuronal cells and nerve fibers, and microstructurally defined 3D maps of brain areas. This requires microscopic imaging and analysis of large quantities of histological brain sections at high throughput, leading to image datasets at the Petabyte scale.

At the intersection of neuroinformatics, computer vision and artificial intelligence (AI), our research addresses

- Data and workflow management for high throughput microscopic imaging,

- Machine learning and computer vision algorithms for biomedical image analysis on high performance computers,

- Software development for structured access to large image datasets and interactive 3D exploration of high-resolution brain atlases over the web.

What infrastructure, programs and tools are used in your group?  The trainee will be part of the local Helmholtz AI unit in Jülich. Our data management and image analysis workflows run on a distributed environment which includes Jülich’s modular high-performance computing systems. Image acquisition of whole-brain sections at micrometer resolution is performed on a high throughput microscopy facility. Outputs of our research are typically integrated with the human brain atlas hosted in the European research infrastructure EBRAINS, to which we contribute key developments. Most of our software is implemented in Python. Image analysis and machine learning approaches use common Deep Learning frameworks, typically PyTorch or Tensorflow.

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

A trainee in our group can learn about

- biomedical computer vision, especially processing and analysis of high resolution image data using state of the art AI methods

- distributed workflow management and parallel processing of big image data on supercomputers

- neuroinformatics.

A trainee can support our group with practical experience in machine learning approaches for image analysis from the point of view of another application domain. A trainee could also contribute with experience in inverse modelling, simulation, or computer graphics.

   

Simon Eickhoff

Forschungszentrum Jülich: Brain and Behaviour (INM-7)

Short summary of your group's research: We develop and apply novel methods to better understand  the organization of the human brain and to relate it to behavioral phenotypes in healthy and as biomarkers in clinical populations. To do so, we capitalize on machine learning approaches. In addition to these scientific objectives, we also develop open access methods and tools for data management and reproducible research.           

What infrastructure, programs and tools are used in your group? Python, R, MATLAB, HPC, machine learning, neuroimaging

What could a participant of the HIDA Trainee Network learn in your group? How could he or she support you in your group?  A HIDA trainee can choose from diverse topics from machine learning, data management to biomarker discovery. For technically oriented trainees we offer opportunities to contribute to our growing set of tools. For application-oriented trainees we offer participation in ongoing projects to uncover brain-behavior relationships.

    

Fabio Fiorani

Forschungszentrum Jülich: Juelich Plant Phenotyping Center/Institute of Bio- and Geosciences, IBG2: Plant Sciences

Short summary of your group's research: We focus on the application and development of high-throughput, non-invasive phenotyping methods for screening of plant shoot and root productivity traits. In our group teams of plant biologists, engineers and image processing specialists cooperate to realize screening methodologies for shoot and root growth and architecture combined with physiological measurements of plant performance and plasticity to environmental challenges. Interpretation of results requires the evaluation of multi-dimensional data structures which include dynamic changes of plant phenotypes over time, environmental and imaging sensors data.

What infrastructure, programs and tools are used in your group? We have developed and operate a unique set of automated experimental platforms to analyze plant phenotypes in a minimally-invasive fashion, please click here to learn more.

What could a participant of the HIDA Trainee Network learn in your group? How could he or she support you in your group?  Participants to the HIDA Trainee Network would benefit from learning: a) methodologies for high-throughput screening in plants; b) data analyses and visualization of complex data sets; c) experimental designs for plant phenotyping.  HIDA Trainee Network participants would support our continued efforts in improving and automating data analyses using existing data stored in several institutional databases. 

   

Eric von Lieres

Forschungszentrum Jülich: 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 Trainee Network 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).

    

Andreas Lintermann

Forschungszentrum Jülich: Simulation and Data Laboratory "Highly Scalable Fluids & Solids Engineering" (SDL FSE), Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation (IAS)

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 Trainee Network learn in your group? How could he or she support you in your group? The student 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 student 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.

   

Wolfgang Wiechert

Forschungszentrum Jülich: 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 Trainee Network 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

   

Thomas White

Deutsches Elektronen-Synchrotron DESYPhoton Science, CFEL Coherent Imaging Division

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 Trainee Network 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.

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