Forschungszentrum Jülich GmbHfz-juelich.de
PhD Position - Representation and Active Learning for Multi-Scale Scientific Imaging
- Location
- Jülich, Aachen
- Remote
- On-site
- Employment type
- Thesis
- Experience
- Junior
Today
Conducting research for a changing society:
this is what drives us at Forschungszentrum Jülich. As a member of the Helmholtz Association, we aim to tackle the grand societal challenges of our time and conduct interdisciplinary research into a digitalized society, a climate-friendly energy system, and a sustainable economy. Work together with some 7,600 employees in one of Europe's biggest research centres and help us to shape change!
The Institute for Materials Data Science and Informatics (IAS‑9) develops advanced Machine Learning & Artificial Intelligence methods tailored to challenges in the physical sciences and engineering, bridging data-driven approaches with domain knowledge to push the boundaries of scientific discovery. Our group brings together ML engineers, AI researchers, data scientists, research software engineers, and domain scientists with a shared focus on scientific machine learning. Together, we develop and apply ML methods to tackle key challenges in the physical sciences and engineering: from accelerating simulations with surrogate models to extracting insights from complex imaging data, and building approaches that transfer across domains.
In addition, we benefit from a strong connection to the Ernst-Ruska-Centre for Electron Microscopy and to the Jülich Supercomputing Center. We are particularly interested in advancing foundational machine learning methods for scientific imaging, with a focus on representation learning and data-efficient decision-making across heterogeneous data sources.
PhD Position – Representation and Active Learning for Multi-Scale Scientific Imaging
The PhD project is methodologically independent and embedded in a multidisciplinary research environment at the interface of artificial intelligence, scientific imaging, and materials research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team of data scientists, software engineers, and experimental researchers on topics including:
- Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e. g., SEM, TEM, EBSD).
Salary for this role
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