Franka Weiler is completing a Master’s degree in Applied and Engineering Physics at the Technical University of Munich and holds a Bachelor’s degree in Physics. Her academic interests lie at the intersection of machine learning, Earth observation and energy research.
For her Bachelor’s thesis at the German Aerospace Center’s Institute of Solar Research in southern Spain, she developed a deep-learning approach to estimate cloud-base height from stereo all-sky imagery. Alongside her studies, she works at TUM on coupling neutron-transport and thermal-hydraulic simulations. She has also studied at Sorbonne Université in Paris.
As part of her Master’s thesis with Φ-lab, Franka works on machine-learning methods for Earth-observation data. Her research includes using satellite time series and foundation model embeddings for applications such as crop-type classification, with a broader focus on developing robust methods for environmental monitoring.
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