Friedrich-Alexander-Universität Erlangen-Nürnberg

Advantage through networking – the leitmotif of the Friedrich-Alexander-University in the Erlangen and Nuremberg region (FAU) can be found in many aspects of the traditional yet modern university philosophy. The university offers 265 courses of study for its currently more than 38,000 students. The main focus is on the personality of each individual, which is to be promoted and strengthened through independent research and early responsibility.

With the founding of the Technical Faculty in 1966, FAU was the first German university to combine the services of a traditional full university with those of a technical university. The Technical Faculty, with more than 10,000 students, works on basic principles as well as on topics relevant to the market and technology. Its scientific focus is on the areas of new materials and processes, life science engineering and medical technology, modeling and simulation, energy technology and mobility, optical technologies, information and communication technology as well as micro- and nanotechnology.

The university not only focuses on the interdisciplinary cooperation of all five faculties but much more on the continuous development and optimization of its application-oriented research and teaching. The success of the university model is reflected in countless high placings in international rankings as well as 200 Humboldt fellows and 272 patent applications in the field of technology in recent years.

So far, the following projects have been realised in cooperation with Friedrich-Alexander-Universität Erlangen-Nürnberg

DEIN-Pro: Digital decision support to integrate sustainability at the process level

Digital decision support to integrate sustainability at the process level (Charlotte Bahr)

PPML4CEN – Federated Learning for Collaborative Enterprise Networks

Federated Learning for Collaborative Enterprise Networks (Stefan Arnold)

RISCoT – Security analysis of trusted runtime environments on RISC-V

Security analysis of trusted runtime environments on RISC-V (Davide Bove)

BePra-MIS – Best Practice Handbook for Microservice Integration

Best Practice Handbook for Microservice Integration (Georg-Daniel Schwarz)

BuBLe – Building Bridges: Reinforcement Learning on Molecular and Process Graphs

Building Bridges: Reinforcement Learning on Molecular and Process Graphs (Maximilian Harl)

APED-F – Analysis of position data to determine throughput times in production

Analysis of position data to determine throughput times in production (Sebastian Volpert)

DeLePred – Deep learning in the context of predictive maintenance

Deep learning in the context of predictive maintenance (Sandra Zilker)