Tasks:
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Design and benchmark deep learning models (CNNs, RNNs, Transformers) for fault detection, classification, and localization in high-voltage power systems.
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Work with high-resolution time-series data (current/voltage signals from simulations).
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Investigate advanced concepts like knowledge distillation, transfer learning, and multi-task learning.
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Analyze robustness to data scarcity, sensor dropout, and noise.
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(Optional) Extend the pipeline for real-time or distributed inference.
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(Optional) Co-author a scientific paper based on your results.
Requirements:
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Strong programming skills in PyTorch
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Experience with training deep learning models
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Ability to attend in-person meetings
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Bonus: Interest in signal processing, ML robustness, or time-series analysis
Application:
Send your application with the subject
“Application Fault DL Thesis + your full name” to julian.oelhaf@fau.de and include:
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Curriculum Vitae (CV)
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Short motivation letter (max. one page)
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Transcript of records
This topic can also be conducted as a smaller project (e.g., research or programming project) instead of a full thesis.