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, to create a unified and reliable representation of structural integrity. The work expands on TU/e’s contributions by developing algorithmic components for detection and classification of defects and anomalies
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representations with limited, weak, or noisy supervision Adapting, specializing, or probing large pre-trained models for domain-specific visual understanding Self-supervised and representation learning for images
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learning video-AI models; b) assess representational alignment of bio-inspired deep learning models to the human brain. The bio-inspired models will be enriched with different temporal integration mechanisms
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will improve the land surface representation of PCR-GLOBWB by including new to be developed soil and vegetation modules and an energy balance. Secondly you will use AI-based surrogates to speed up