Helmholtz AI Consultant Team for Matter Research
Helmholtz AI aims to empower scientists to apply machine learning methods to their scientific problem domains. This goal will be achieved by fostering and stimulating collaborative interdisciplinary research projects; by leveraging similarities between data-driven solutions across domains; by integrating field-specific excellence and AI/ML prowess; by improving the quality, scalability and timely availability of emerging methods and tools and by training the current and next generation of scientists in using AI methods and tools.
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. Nico Hoffmann 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. This page introduces the Helmholtz AI Consultant Team.
"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 want to disseminate state-of-art best practises in ML and data science. With this, we hope to boost data understanding of our clients at the global academic scale in order to provide a competitive advantage. Within this mandate, we will try to advance methods or tooling in order to reduce the time investment on our as well as on our clients' side."
You, your data and us
It is our task to aid scientists with their needs to process small and big data. For this, we offer in-person consulting as well as collaborative projects, i.e. vouchers. A voucher is meant to guarantee a fair and uniform processing of projects at HZDR and other Helmholtz centers across Germany. Therefor, a voucher must comply to the following criteria:
- It should describe a feasible goal which can be achieved by state-of-the art machine learning methods and assets of artificial intelligence.
- It should describe a project, that can be concluded within a period of 2 weeks or upto 6 months.
- It should report and link to data, that can be used to train and use state-of-the art machine learning methods.
- It should define uncertainty bounds that current method obtain and a possible AI agent should improve on.
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.
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- automated pipelines
- image processing
- reproducible environments
- denoising of (image) data with Deep Learning methods
- Deep Learning to solve partial differential equations
- pattern recognition with Deep Learning
- uncertainties of Deep Learning networks
- fast inference of Deep Learning networks