Technische Universität Dresden

The Technische Universität Dresden, in the city of art and science, has completely reorganized its 17 faculties in five fields in order to improve and simplify interdisciplinary cooperation. Hardly any other German university can offer such an integrated range of courses as TUD: here engineering sciences, mathematics and natural sciences, humanities and medicine are all under one roof. A special focus of the university is on the individuality of each of the over 30,000 students. That’s why they can apply for any of the 119 courses of study to further develop their own personality. The university is committed to equal opportunities and work-life balance. For example, female students are supported in order to increase their numbers at the university, especially in the fields of computer science, engineering and natural sciences. Moreover, the Technical University of Dresden particularly supports young families in reconciling studies and family life. As a result, it has been certified as a family-friendly university since 2007.

The university is one of the eleven universities of excellence in Germany and pursues its concept for the future by “building bridges”, which is intended to connect not only people, but also science, business, East and West, as well as generations through education. The TU Dresden is a partner in the DRESDEN concept – a unique network in Germany to which all four major German scientific societies (Fraunhofer Association, Max Planck Association, Leibniz Association and Helmholtz Association) belong. The synergy with non-university institutions, the interlinking with industry and interdisciplinary research and teaching play an extremely important role.

With more than 1,600 students, the Faculty of Computer Science is one of the largest educational institutions for computer science in Germany. Its research interests include software technology, the Internet of Services, cloud computing and security, data-intensive computing and big data, knowledge extraction, human-computer interaction and visual computing, formal modeling and analysis, and machine learning and simulation.

© C. Vojta

So far, the following projects have been realised in cooperation with TU Dresden

EVIDENZ: Evidence-based text generation using LLMs

Evidence-based text generation using LLMs (Tobias Schreieder)

RAILsilient: Rail-to-Orbit Communication for Automated and Resilient Railway Operation

Rail-to-Orbit Communication for Automated and Resilient Railway Operation (Mario Franke)

LLM4Edu: Learning Large Language Models for Education

Learning Large Language Models for Education (Shuzhou Yuan)

MoMiKoSy: Development of a Molecular, Microfluidic Communication System

Development of a Molecular, Microfluidic Communication System (Pit Hofmann)

X-DNet: Energy-Efficient Distributed and In-Network Computing

X-DNet: Energy-Efficient Distributed and In-Network Computing via Approximation of Applications and Accelerators (Zahra Ebrahimi)

RISC-ARA: 5G/6G channel estimation hardware acceleration with the RISC-V architecture

5G/6G channel estimation hardware acceleration with the RISC-V architecture (Javier Acevedo)

KIKS: AI components for production plant simulation

AI components for production plant simulation (Lucas Woltmann)

R-SLAM: Localization for Automated Guided Vehicles with 5G and Multi-Access Edge Computing

Localization for Automated Guided Vehicles with 5G and Multi-Access Edge Computing (Peter Sossalla)

SARA: Safety-Aware Relocation of functions in a multi-core computer Architecture

Safety-Aware Relocation of functions in a multi-core computer Architecture (Behnaz Ranjbar)

Net-BliSS – In-network Blind Source Separation Enabled Acoustic Anomaly Detection for Ultra-Reliable and Low-Latency Communications Applications

In-network Blind Source Separation Enabled Acoustic Anomaly Detection for Ultra-Reliable and Low-Latency Communications Applications (Huanzhuo Wu)

DiscoRet – Automatic discovery of pathological features in retina with unsupervised deep neural networks

Automatic discovery of pathological features in retina with unsupervised deep neural networks (Nishant Kumar)

S³ – Building Blocks for Security in Industrial IoT

Building Blocks for Security in Industrial IoT (Tim Lackorzynski)

RESP – Roundtrip engineering of recipes for batch and continuous processes in the process industry using a synchronization network

Roundtrip engineering of recipes for batch and continuous processes in the process industry using a synchronization network (Julian Rahm)

REGROW – Resilient routing in low-power mesh networks

Resilient routing in low-power mesh networks (David Nophut)

DESERVE: builDing rEliableSystEms foR V2X nEtworks

builDing rEliableSystEms foR V2X nEtworks (Sreekrishna Pandi)