Getting a Doctorate with the Department

Doctoral Program 'Applied Computer Science and Business Informatics'

The doctoral program investigates applied computer science problems from an inter- and transdisciplinary perspective. It reflects the department’s key research ares in ‘Cyber Security’, ‘Visual Computing’, and ‘Business Informatics’, and is primarily aimed at doctoral candidates with degrees in engineering, natural sciences, social sciences, or business informatics. Accordingly, graduates may be awarded the degree Dr.-Ing., Dr. rer. nat., or Dr. rer. pol. The specific doctoral degree awarded depends on the thematic focus of the dissertation.

The program emphasizes the development of a broad perspective on methodological foundations and disciplinary as well as societal interconnections that go beyond the individual key research areas of ‘Cyber Security’, ‘Visual Computing’, and ‘Business Informatics’.

Doctoral Program 'AI and Data Science'

The doctoral program focuses on research into machine learning (ML) and artificial intelligence (AI) methods for applications in mechanical engineering, industrial engineering, automation technology, and the life sciences. It reflects the department’s key research area ‘Data Science’ and is primarily aimed at doctoral candidates from computer science and related disciplines, including information science, mathematics, physics, mechanical and industrial engineering, statistics, automation and electrical engineering, as well as the geo- and life sciences. Accordingly, a solid foundation in mathematics and strong knowledge in specific areas of computer science, statistics, and the relevant application domains are essential prerequisites. Depending on the focus of the dissertation, graduates may be awarded the degree Dr.-Ing. or Dr. rer. nat.

The program addresses scientific and technical approaches for generating knowledge from data, particularly in cases where traditional applied computer science methods reach their limits or require automation.

Key areas include data collection and preparation, modeling and simulation, analysis and optimization, as well as evaluation and archiving of large, heterogeneous datasets. Through these approaches, innovative technologies are applied in interdisciplinary contexts to create high-quality and efficient solutions at the interface of computer science, natural and engineering sciences, humanities and social sciences, and industry. Methods from ‘Artificial Intelligence’ and ‘Data Science’ are essential for this purpose.