Researching and extracting industrial and commercial buildings across Germany using open-source geospatial data (e.g., OpenStreetMap, CORINE Land Cover, Sentinel, etc.)
Applying machine learning / AI and/or statistical algorithms to classify building and land-use types relevant to electrical consumption
Labeling and preparing training data for AI models; developing automated pipelines for classification
Integrating findings into the existing package synthetic grid tool for generating realistic medium-voltage distribution grids
Collaborating with domain experts from energy modeling, geoinformatics, and data science
Preparing scientific documentation of your methodology and results
Contributing to writing scientific papers for internal reporting and possible publication
Required qualifications:
Very good performance in your master’s studies in electrical engineering, computer science, geoinformatics, energy systems, or a related field
Solid programming skills in Python and familiarity with machine learning libraries (e.g., scikit-learn, TensorFlow, and PyTorch)
Experience in working with geospatial data (e.g., GeoPandas, Rasterio, and Shapely)
Interest in AI and energy systems modeling
Ability to communicate and document research results clearly in English (B2)
Desirable qualifications:
GIS experience (QGIS, GDAL), basic understanding of distribution grid concepts and tools, like PyPSA
Experience with academic writing or contributions to scientific papers
High level of independence, motivation, and a structured, reliable work approach
Good team skills and willingness to engage in interdisciplinary collaboration
Please feel free to apply for the position even if you do not have all the required and desirable skills and knowledge.
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