The University Of Augsburg (UAU), founded in 1970, is a prestigious public institution in Augsburg, Bavaria, Germany. Renowned for its interdisciplinary approach to education and research, the university offers a wide array of undergraduate and graduate programs in humanities, social sciences, natural sciences, and engineering. A key research group within the university is the Chair of Embedded Intelligence for Health Care and Wellbeing (EIHW), which focuses on the development and application of artificial intelligence (AI) techniques, particularly deep learning, to address critical challenges in healthcare and wellbeing. This group’s research endeavors have a significant impact on AI-driven solutions for disease diagnosis, treatment, rehabilitation and health monitoring. Areas of expertise include affective computing, human activity recognition, and physiological signal processing. The UAU actively contributes to the global AI research community, producing high-quality research publications and fostering collaborations with various internal and external partners. By engaging in interdisciplinary research, the Chair of Embedded Intelligence for Health Care and Wellbeing demonstrates a strong commitment to advancing the AI field and developing innovative solutions with real-world impact.
Role in the project:
The University Of Augsburg (UAU) will play a prominent role in this project, leading and contributing to multiple tasks that involve natural language processing (NLP), deep learning algorithms, and multimedia content analysis. As a leader in several tasks, UAU will develop comprehensive textual representations of cultural heritage (CH) assets, create linguistic models for temporal evolution, and devise mechanisms for time-independent curation of these assets. Leveraging its expertise in NLP and deep learning, UAU will focus on advancing state-of-the-art techniques to deliver impactful outcomes in these areas. Moreover, UAU will lead the development of multimedia content analysis algorithms, suitable for processing cultural archives and enabling content enrichment, semantic segmentation, and storytelling. By investigating and extending existing deep learning architectures, UAU aims to maximise salient information extraction from meanly textual modalities. UAU will also contribute on assisting the development of interfaces capable of integrating with existing cultural asset repositories, content management systems, and digital rights management systems.