Contact
Photo: HZDR/A. Wirsig
Dr. Peter Steinbach
Head Group of Artificial Intelligence
p.steinbach
hzdr.de
Phone: +49 351 260 3844
Helmholtz AI Consultant Team
Team Mandate
"The Helmholtz AI consultants team's mission at HZDR is to consult scientists primarily of the research field Matter in the application of automated data processing and knowledge extraction methods. We disseminate state-of-art best practises in ML and data science. We work as collaborators embedded into partnering teams for the time of a collaboration.
With this, we aspire to boost data understanding of our scientific collaborators at the global academic scale in order to provide a competitive advantage. Within this mandate, we advance methods or tooling in order to reduce the time investment on our as well as on our collaborators' side."
You want to use AI and are Helmholtz staff?
We offer in-person free-of-charge consulting and collaborative projects, i.e. vouchers, with any member of Helmholtz in matter research. A voucher is meant to guarantee a fair and uniform processing of projects at HZDR and other Helmholtz centers across Germany. An idea can become a voucher if:
- [Goal] Your idea describes a feasible goal which can be achieved by state-of-the art machine learning methods and assets of artificial intelligence.
- [Time] Your idea describes a project, that can be concluded within a period of 2 weeks or upto 6 months.
- [Data] You can report and link to data, that can be used to train and use state-of-the art machine learning methods.
These vouchers are defined and created in collaboration with us. After that, they are submitted into a light-weight review process. The central administration of Helmholtz AI, other consultant teams as well as the central Helmholtz Office in Berlin will review the voucher and potentially approve it for action.
You have Questions or Ideas? Contact Us: consultant-helmholtz.ai@hzdr.de
You are a student?
Looking for a student job, internship or master thesis topic? Feel free to contact us! We offer
- a solid, inspiring and relevant task,
- dedicated mentoring,
- access to performant hardware resources,
- work on open-source software tools,
- flexible working hours,
- possibility to publish your work,
- optional financial support
As the list of possible topics changes, we can only provide indications on what we can offer presently. Please contact us to have a discussion what topic aligns with your interests and your wish to expand your skills:
- Uncertainty Quantification in Computer Vision Models, Large Language Models or Surrogate Models
- Simulation-based Inference as a AI guided tool to infer simulation parameters from data (images, tables)
- Multi-Modal Foundational Models in Earth Sciences, Physics and other fields (images, text, tables, ...)
- Large Language Models for Knowledge Extraction in Scientific Texts (text)
- Methods for Anomaly Detection in Particle Accelerator Monitoring Data (multivariate imte series)
- Pattern Extraction and Segmentation in Images and Videos of fluid dynamics and life science experiments (images, videos, volumes)
Helmholtz AI is our Network
The mandate of Helmholtz AI: We democratize AI and advance its capabilities for tackling frontier scientific questions while harnessing AI's potential for solving the grand challenges of research and society. We are an application-driven artificial intelligence platform accelerating science across the Helmholtz Association. We enable the development and implementation of AI solutions while promoting collaboration and ensuring access to resources and expertise.
These goals will be pursued beyond institute and center limits by regular funding opportunities and collaboration-as-a-service offerings as well as many more activites. At HZDR, a Young Investigator Gruppe led by Dr. Jeffrey Kelling and a Helmholtz AI Consultant Team led by Dr. Peter Steinbach have been installed. Both build the Helmholtz AI Local Unit to support all scientists within the research field matter of the German Helmholtz Association.
Our Team
| name | building/room | +49 351 260 .... | |
| Maleeha Hassan | 270/212 | m.hassan@hzdr.de | |
| Helene Hoffmann | 270/212 | 3849 | helene.hoffmann@hzdr.de |
| Gunjan Joshi | 270/221 | g.joshi@hzdr.de | |
| Israt Jahan Tulin | 270/212 | i.rulin@hzdr.de | |
| Ulrik Günther | 270/212 | ulrik.guenther@hzdr.de | |
| Till Korten | 270/212 | t.korten@hzdr.de | |
| Steve Schmerler | 270/212 | 3856 | s.schmerler@hzdr.de |
| Sebastian Starke | 270/212 | 3693 | s.starke@hzdr.de |
| Peter Steinbach | 270/221 | 3844 | p.steinbach@hzdr.de |
Our Expertise / past vouchers
Ion-beam analysis spectrum evaluation
| Description: | Ion beam analysis is a method to analyse the surface near elemental and isotopic composition of practically any target material with a tomographic information. The evaluation of ion-beam analysis spectra so far requires extensive manual input, strongly limiting the methods throughput. Simple chi2 based optimisation methods such as simplex, implemented in physics models such as SimNRA, hardly allow finding global minima of the spectral evaluation due to the many involved parameters and non-linearities. The physics models allow for generating huge amounts of synthetic data with high quality via forward calculations, though. This opens ups the possibility for deep learning algorithms to learn evaluating even complex spectra and assist the optimisation methods in finding close-to-final initial values or even replacing the given tool for finding final results. |
| Expected Results (Goals): |
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| Owner: | Sören Möller |
| Client: | FZ Jülich |
| Project Page: | gitlab.hzdr.de/haicu/vouchers/fz_juelich/fzj_moeller_ion_beam_analysis |
NTK Based Parameter Annealing
| Description: |
Physics-informed neural networks (PINNs) are learning-based algorithms for solving partial differential equations (PDEs). Their training involves the minimization of a physically-informed loss function using stochastic gradient descent. The loss function integrates prior knowledge of the reformulated PDEs which is iteratively minimized. However, despite their empirical success, little is known about their behavior during training. Moreover, due to unknown reasons sometimes they fail to converge. This project aims to investigate the behavior of PINNs using Neural Tangent Kernels (NTK). NTK captures the behavior of fully-connected neural networks in the infinite width limit during training via gradient descent. The limiting NTK can be used to provide insights into the training dynamics of PINNs. The eigenvalues of the NTK can help anneal hyperparameters to recalibrate the convergence rate of PINNs as a result. In this project, the aim is to leverage the loss functions of PINNs to solve different PDEs by the integration of NTK. |
| Expected Results (Goals): |
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| Owner: | Nico Hoffmann |
| Client: | FWKT |
| Project Page: | https://gitlab.hzdr.de/haicu/vouchers/hzdr/hoffma83-ntk-paramopt |
Instance Segmentation in Videos of Bright-Field Microscopes
| Description: |
Here we explore the capabilities of algorithms designed for video instance segmentation (VIS) or multi-object tracking (MOT) for segmenting overlapping bubbles in video data. To accurately detect bubbles in experimental studies, AI-assisted segmentation of overlapping bubbles in acquired images is becoming more and more standard. Although there are several promising approaches for this task, they have problems at high levels of overlap and are often unable to reconstruct the hidden part of the occluded bubble. Moreover, such segmentation is not able to detect fully occluded bubbles in an image. The use of video sequences can help to solve these problems, since partially or completely occluded bubbles are often more visible in an image just before or after the initial image. |
| Expected Results (Goals): |
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| Owner: | Hendrik Hessenkemper |
| Client: | HZDR, FWD |
| Project Page: | https://gitlab.hzdr.de/haicu/vouchers/hzdr/hzdr_hessen93_vis_mot_bubbles |
You have Questions or Ideas? Contact Us: consultant-helmholtz.ai@hzdr.de
