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UH

Universität Hamburg
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353 Projects, page 1 of 71
  • Funder: European Commission Project Code: 101072488
    Funder Contribution: 2,062,400 EUR

    TRAIL strategically focuses on a novel, highly interdisciplinary and cross-sectorial research and training programme for a better understanding of transparency in deep learning, artificial intelligence and robotics systems. In order to train a new generation of Doctoral Candidates to become experts in the design and implementation of transparent, interpretable neural systems and robots, we have built a highly interdisciplinary consortium, containing expert partners with long-standing expertise in cutting-edge artificial intelligence and robotics, including deep neural networks, computer science, mathematics, social robotics, human-robot interaction and psychology. In order to build transparent robotic systems, these new ESR researchers need to learn about the theory and practice of the principles of (1) internal decision understanding and (2) external transparent behaviour. Since the ability to interpret complex robotic systems needs highly interdisciplinary knowledge, we will start, on the decision level, to interpret deep neural learning and analyse what knowledge can be efficiently extracted. At the same time, on the behaviour level, the disciplines of human-robot interaction and psychology will be key in order to understand how to present the extracted knowledge as behaviour in an intuitive and natural way to a human user to integrate the robot into a cooperative human-robot interaction. A scaffolded training curriculum will guarantee that the ESRs have not only a deep understanding of both research areas, but experience optimal skill training to be fully prepared for a successful research career in academia and industry. The importance and need of this research for the industry is clearly visible with the full commitment of 7 leading European and world-wide-operating robotics companies that together cover the majority of Europe’s robot market and a broad spectrum of AI applications.

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  • Funder: European Commission Project Code: 339470
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  • Funder: European Commission Project Code: 881603
    Overall Budget: 150,000,000 EURFunder Contribution: 150,000,000 EUR

    This proposal describes the third core project of the Graphene Flagship. It forms the fourth phase of the FET flagship and is characterized by a continued transition towards higher technology readiness levels, without jeopardizing our strong commitment to fundamental research. Compared to the second core project, this phase includes a substantial increase in the market-motivated technological spearhead projects, which account for about 30% of the overall budget. The broader fundamental and applied research themes are pursued by 15 work packages and supported by four work packages on innovation, industrialization, dissemination and management. The consortium that is involved in this project includes over 150 academic and industrial partners in over 20 European countries.

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  • Funder: European Commission Project Code: 226487
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  • Funder: European Commission Project Code: 2020-1-DE01-KA107-005397
    Funder Contribution: 178,340 EUR

    This is a project for higher education student and staff mobility between Programme Countries and Partner Countries. Please consult the website of the organisation to obtain additional details.

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