The Max-Planck-Institut für Eisenforschung GmbH offers two Postdoc positions (data analytics in material science) on

1. Automatic classification and feature extraction from multi-dimensional STEM data in the context of scanning transmission electron microscopy (STEM) on advanced materials.

2. Improved reconstruction algorithms for Atom Probe Tomography

Modern scanning transmission electron microscopy (STEM) instrumentation generates large multi-dimensional and multi-channel data sets. Likewise, atom probe tomography data sets contain spatially resolved detector hits for several hundred million ions. Analysing these data sets 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 improved algorithms, we seek postdoctoral researchers 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 groups (STEM: C. Liebscher, APT: B. Gault), the Computational Material Design department, and the Fritz-Haber-Institut of the Max-Planck Society (L. Ghiringelli, A.Ziletti) 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  made via our application management system

STEM: https://recruitingapp-5424.de.umantis.com/Vacancies/285/Description/2

APT: https://recruitingapp-5424.de.umantis.com/Vacancies/286/Description/2