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for the materiality of the built environment in defined regions, based on MFA and supported by BIM, GIS, IoT and AI technologies. Map existing anthropogenic material stocks and their dynamics and simulate circularity
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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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(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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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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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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production, agriculture broadly, and/or smart technologies is desirable. • Experience in modelling biological or agricultural systems, with strong programming skills (R, Python, or Matlab