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(https://www.ise.fraunhofer.de/en/research-projects/pvev.html ), we are working to optimize models for PV self-consumption estimation and, on that basis, to develop an algorithm for PV feed-in upscaling
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affect resource availability and infrastructure. This thesis will explore how disruptive scenarios impact renewable energy potentials across Germany, by combining Python-based GIS workflows with energy
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and AI algorithms Solid programming skills in Python and familiarity with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) Experience working with geospatial data (e.g., geopandas
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programming skills in Python and R Experience in software development, particularly in dashboard or web application creation Familiarity with geospatial analysis, GIS tools, or related technologies is a plus
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qualifications in probilistic risk modelling, applied statistics and familiar with quantitative risk modelling measures strong data analysis skills and proficiency in R and Python; experience with other programing
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good performance in your Master’s studies in Electrical Engineering, Computer Science, Geoinformatics, Energy Systems, or related field Solid programming skills in Python and familiarity with machine
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production, agriculture broadly, and/or smart technologies is desirable. • Experience in modelling biological or agricultural systems, with strong programming skills (R, Python, or Matlab