PhD Position – Representation and Active Learning for Multi-Scale Scientific Imaging

Shaping change: this is what drives us at Forschungs­zentrum Jülich. As a member of the Helmholtz Association with some 7,600 employees, we conduct interdisciplinary research into a digitalized society, a climatefriendly energy system, and a sustainable economy. We focus on the natural, life, and engineering sciences in the fields of information, energy, and bioeconomy. We combine this with expertise in high‑performance computing and artificial intelligence using unique scientific infrastructures. 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

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