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Simon Müller


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Simon Müller joined the eScience group of BAM in 2023 as a research assistant working in the TREEADS project, in which he develops machine learning models to predict wildfire spread and susceptibility with remote sensing data.
Simon studied geosciences at the Friedrich-Alexander University Erlangen-Nürnberg, where he first got in contact with machine learning and remote sensing. After graduation in 2018, he worked at the Federal Institute for Geosciences and Natural Resources (BGR) in the projects GeoLIBScanner and T-REX. In both projects, he focused on the application of machine learning algorithms for mineral classification, as well as element detection and quantification on large-scale, high-resolution Laser-Induced Breakdown Spectroscopy (LIBS) measurements of drill core samples. Following this work, he did his PhD at the Leibniz University Hannover.


  • Müller S, Meima JA, Gäbler, H-E. Improving spatially-resolved lithium quantification in drill core samples of spodumene pegmatite by using laser-induced breakdown spectroscopy and pixel-matched reference areas. Journal of Geochemical Exploration, 2023, 250. Jg., S. 107235. doi:
  • Müller S, Meima JA. Mineral classification of lithium-bearing pegmatites based on laser-induced breakdown spectroscopy: Application of semi-supervised learning to detect known minerals and unknown material. Spectrochimica Acta Part B: Atomic Spectroscopy, 2022, 189. Jg., S. 106370. doi:
  • Müller S, Meima JA, Rammlmair, D. Detecting REE-rich areas in heterogeneous drill cores from Storkwitz using LIBS and a combination of k-means clustering and spatial raster analysis. Journal of Geochemical Exploration, 2021, 221. Jg., S. 106697. doi: 10.1016/j.gexplo.2020.106697