The Max-Planck-Institut für Eisenforschung GmbH in Düsseldorf (Germany) offers a Postdoc position (theory/simulation) on "Automatic classification and feature extraction from multi-dimensional STEM data" in the context of scanning transmission electron microscopy (STEM) on advanced materials.

Modern scanning transmission electron microscopy (STEM) instrumentation generates large multi-dimensional and multi-channel data sets. Analysing these datasets takes much longer than the actual experiments because the complex interplay of data accumulation and reduction, noise filtering, feature detection, and sample-specific interpretation requires a lot of human tuning.

Within the recently founded Max-Planck BigMax network on Big Data analytics in Material Science, we develop methods to automatize this complex analysis with machine-learning and statistical techniques, to provide high-level analysis tools. For developing unsupervised feature detection algorithms for high-resolution STEM, we seek a postdoctoral researcher with a strong background in material science, physics, chemistry, or a related field. The project requires good programming skills in Python or C++. Prior experience with machine learning is a plus, but not required.

The work will be in close collaboration with our in-house experimental STEM groups (C. Liebscher), the Computational Material Design department, and the Fritz-Haber-Institut of the Max-Planck Society (A.Ziletti, L. Ghiringelli) and therefore offers unique opportunities to connect to leading experts. We offer an exciting, collaborative, and dynamic environment and excellent access to computational and intellectual resources.

Applications should be sent in electronic form to Christoph Freysoldt [email protected] and include a CV, copy of master and PhD certificates, and list of publications. The Max-Planck-Institut für Eisenforschung GmbH is committed to employing more handicapped individuals and especially encourages them to apply. Furthermore we seek to increase the number of women in areas where they are underrepresented and therefore explicitly encourage women to apply.