Search

Barcelona, Spain, 2025, pp. 77-83, doi: 10.1109/ICMERR64601.2025.10949991. Briechle, Dominique and Rausch, Andreas. 2024. You’ve Got a Plan? A Domain Modelling Approach for Collaborative Product Disassembly Planning […] 2308-4499) Mohammed Fahad Ali, Dominique Briechle, Marit Briechle-Mathiszig, Tobias Geger, and Andreas Rausch. 2024. Party Without a Cake? Onto an Inter-modal HitchHike Logistics Platform for Passengers […] Publications Paper M. F. Ali, D. F. Briechle, M. E. A. Briechle-Mathiszig, T. T. M. Geger, und A. Rausch. Repairing is Caring - An Approach to an AI-Supported Product-Service-System for Bicycle Lifecycle

engineering but also from other application domains. Project information Contact: Steffen Küpper Andreas Rausch Projekt duration: 01/2017-12/2019 Supported by the EFRE Project sponsor Project partner TU Clausthal

measure and assed data quality. Project information Contact: Sebastian Lawrenz Priyanka Sharma Andreas Rausch Project duration: 06/2018-05/2021 Supported by the ESF Project sponsors Project partners TU Clausthal

Beteiligung regionaler Technologienunternehmen. Projektinformation Ansprechpartner: Meng Zhang Andreas Rausch Projektlaufzeit: 04/2019-03/2022 Supported by the EFRE Project sponsor Projektpartner: TU Clausthal

AI4SSE: ML and LLMs-Enhanced Software and Systems Engineering Modern software and systems engineering face challenges due to increasing complexity and long project lifecycles. Integrating AI offers tr

AI4SSE: ML and LLMs-Enhanced Software and Systems Engineering Modern software and systems engineering face challenges due to increasing complexity and long project lifecycles. Integrating AI offers tr

DGT: Digitized Green Tech The Research Group The Circular Economy has long been cited as the flagship model for systems that can guarantee both monetary value preservation and the compatibility of eco

DGT: Digitized Green Tech The Research Group The Circular Economy has long been cited as the flagship model for systems that can guarantee both monetary value preservation and the compatibility of eco

ML4E: Machine Learned Models for Engineers Machine Learned Models for Engineers For complex technical systems, it is difficult to create complete physical models. Even if this succeeds, the computatio

ML4E: Machine Learned Models for Engineers Machine Learned Models for Engineers For complex technical systems, it is difficult to create complete physical models. Even if this succeeds, the computatio