Machine Learning in Engineering

The Machine Learning in Engineering (MLE) initiative integrates competencies and activities in the field of machine learning at Hamburg University of Technology (TUHH) and partner organizations from the Hamburg metropolitan area. Students, doctoral candidates, postdocs, researchers, and professors from all departments at the TUHH and from the partners are working interdisciplinary together within this initiative. The aim is to conduct fundamental research particularly relevant for the development of new technology, thereby contributing to the digital transformation of the engineering sciences. In addition to basic research, the initiative aims at transferring knowledge to business and industry. Among a number of instruments for this kind of transfer, the annually organised MLE days provide a natural opportunity for education and knowledge exchange.

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Latest News

14.11.24
On October 24th, the 1st Junior Science Café on the topic "Opportunities and Risks of Artificial Intelligence" took place at the "Gymnasium Lüneburger Heide". This event was organized in cooperation with “Wissenschaft im Dialog”. Students from the school organized all aspects of the event, from inviting external experts to press work, content preparation and moderation. The invited experts including MLE member Jens-Peter Zemke discussed current topics in artificial intelligence. For further information (in German) please visit: https://www.glh.de/spannende-gespraeche-beim-1-junior-science-cafe/
11.10.24
The European Doctoral Network PATTERN for Enabling Artificial Intelligence for Electromagnetic Compatibility was granted by the EU in summer 2024. The consortium consists of 9 universities and 12 industrial partners. At TUHH the two MLE members Christian Schuster and Matthias Mnich are part of this network and they will supervise two PhD students working on signal integrity and electromagnetic interference prediction and optimization. For further information please visit: https://pattern-dn.eu/
15.07.24
Sensor Fusion for the Robust Detection of Facial Regions of Neonates Using Neural Networks
08.07.24
A Simple Model for Collaborative Policies in Reinforcement Learning
01.07.24
Generalizability of LLMs and Feature-based Active Learning