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Prof. Dr. Dieter Hutter

Organizational unit Cyber-Physical Systems
Contact +49 421 218 59831 (Bremen)

http://www.dfki.de/~hutter
Address (Bremen) Mehrzweckhochhaus (MZH)Bibliothekstraße 528359 Bremen

Publications

All publications

Profile

Dieter Hutter is vice director of the Cyber-Physical Systems department at the German Research Center for Artificial Intelligence (DFKI) and Honorary Professor at the Bremen University. He received the Diploma and Dr. rer. nat. degrees in computer science from the University of Karlsruhe, Germany, in 1983 and 1991, respectively. He works in the areas of security, formal methods and change management.

Dieter Hutter has been and still is a member of the Program Committees of numerous conferences and workshops and member of the editorial board of the Journal of Applied Logics, and co-initiator of the DFG Priority Programme 1496 on Reliabably Secure Software Systems.

  • REST - SharePort

    Resilienter Smartport - Dynamische Ressourcenteilung in Häfen für robuste Logistikketten

    Das Ziel des Projekts ist eine Stärkung der Resilienz der Bremischen Häfen durch die Kooperation der Akteure im Sinne des Resource-Sharings. Dies soll den Hafenakteuren erlauben, temporär nicht…

  • InfraSLL

    Infrastruktur für Smartport Living Lab (DFKI)

    Bremen's program for the European Regional Development Fund (ERDF) 2021-2027 ("efre-bremen.de": www.efre-bremen.de) has formulated the political objective "A more competitive and smarter…

    InfraSLL

  • InSPoC-3

    InSPoC-3

    In-Space Proof-of-Concept 3 is the third milestone in ESA’s in-space transportation roadmap, aiming at demonstrating the key enabling capabilities for on-board & shared intelligence, culminating at an…

    InSPoC-3

  • RESI-TSN

    Resiliente intelligente TSN-Netzwerke

    Als eine Reihe von neuen Ethernet-Unterprotokollen zur Behandlung von Echtzeitanforderung, ermöglicht es Time Sensitive Networking (TSN) sowohl Standard IP-basierte Datenverkehre als auch…

    RESI-TSN

  • Fast&Slow

    Combination of Symbolic and Subsymbolic Methods

    Deep learning methods are used in many application areas and work very efficiently after a training phase. However, in general no reliable

    statement can be made about their correctness. In contrast,…

    Fast&Slow
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