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physics, engineering, computer science, or a related field. Strong scientific programming in Python and experience with GPU processing of large-scale datasets. Experience with inverse problems and 3D
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for tomography, laminography, or a closely related modality. Strong scientific programming in Python and experience with GPU processing of large-scale datasets. Excellent written and oral
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algorithms. Proven experience with reinforcement learning algorithms and implementations. Experience in programming, e.g., in Python, PyTorch, TensorFlow, or similar. Curiosity and willingness to
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in at least one programming language for geospatial analysis: Python (rasterio, geopandas, scikit-learn), R or Google Earth Engine. Experience with open-source tools (QGIS, GRASS GIS, GDAL) and