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that capture ILC-specific morphology and biology while remaining robust to differences between hospitals, scanners, staining procedures and protocols. The researcher will investigate self-supervised and
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information formats are often fragmented, non-standardized, and not tailored to different user groups, hindering rapid localization and contextual use of instructions, especially in real‑world repair
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-computer interfaces, audio signals for keyword spotting and artificial cochleas, and tactile signals for robot perception. Various types of bio-inspired mechanisms have been investigated in recent years
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the running infrastructures. Using cutting-edge computer vision, wearable sensors, and citizen science, RUN2GETHER will capture large-scale, real-world data during running events and group runs. These data will
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cutting-edge computer vision, wearable sensors, and citizen science, RUN2GETHER will capture large-scale, real-world data during running events and group runs. These data will advance our understanding