Location & approach

By car

The campus of TU Dortmund University is located close to interstate junction Dortmund West, where the Sauerlandlinie A 45 (Frankfurt-Dortmund) crosses the Ruhrschnellweg B 1 / A 40. The best interstate exit to take from A 45 is “Dortmund-Eichlinghofen” (closer to South Campus), and from B 1 / A 40 “Dortmund-Dorstfeld” (closer to North Campus). Signs for the university are located at both exits. Also, there is a new exit before you pass over the B 1-bridge leading into Dortmund.

By train

TU Dortmund University has its own train station (“Dortmund Universität”). From there, suburban trains (S-Bahn) leave for Dortmund main station (“Dortmund Hauptbahnhof”) and Düsseldorf main station via the “Düsseldorf Airport Train Station” (take S-Bahn number 1, which leaves every 15 or 30 minutes). The university is easily reached from Bochum, Essen, Mülheim an der Ruhr and Duisburg.

The H-Bahn

The H-Bahn is one of the hallmarks of TU Dortmund University. There are two stations on North Campus. One (“Dortmund Universität S”) is directly located at the suburban train stop, which connects the university directly with the city of Dortmund and the rest of the Ruhr Area. Also from this station, there are connections to the “Technologiepark” and (via South Campus) Eichlinghofen. The other station is located at the dining hall at North Campus and offers a direct connection to South Campus every five minutes.

By plane

The AirportExpress is a fast and convenient means of transport from Dortmund Airport (DTM) to Dortmund Central Station, taking you there in little more than 20 minutes. From Dortmund Central Station, you can continue to the university campus by interurban railway (S-Bahn). A larger range of international flight connections is offered at Düsseldorf Airport (DUS), which is about 60 kilometres away and can be directly reached by S-Bahn from the university station.

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Doctoral examination of Mr. Sascha Mücke

05/27/2025

We would like to congratulate Mr. Sascha Mücke on his successful doctoral examination on the topic "Quantum-Classical Optimization in Machine Learning".