The Ruhr area, one of Europe‘s largest metropolitan regions, is home of the University Alliance Ruhr (UAR) with a community of 120,000 students and 14,000 researchers. In 2021, the UAR established the Research Center Trustworthy Data Science and Security (RC Trust) in order to enable research that connects psychology, computer science, statistics and cyber security at the intersection of technology, humans and society. The Research Center is seeking to fill the following position at the Faculty of Computer Science, Ruhr-Universität Bochum, Germany:
Associate or Full Professorship for Fairness and Transparency (Open Rank)
We welcome applicants with a strong interest in interdisciplinary research. Candidates should have an excellent track record in at least one of the following areas:
- Trustworthy Machine Learning for Privacy & Security
- FAccT (Fairness, Accountability, Transparency)
- Technology Policy, Privacy Law & Data Science
- Ethics & AI
- Human-AI Collaborative Decision Making.
The professorship will be associated with the Faculty of Computer Science and the Cluster of Excellence „CASA: Cyber Security in the Age of Large-Scale Adversaries“. In addition, we encourage collaboration with the Max Planck Institute for Security and Privacy (MPI-SP). These outstanding research institutions position Bochum as one of the leading research locations for IT Security in Europe.
Appointments will be made for full professorship, or as assistant/associate professorship with tenure track to full professorship. Salaries and working conditions are internationally very competitive and come with a status as civil servant. Full professorships are chair positions that come with phd/postdoc positions, a secretary and a start up package (all negotiable). *
The official job add can be found here. Applications with the usual documents are requested by August 9, 2022 to: career(at)casa.rub.de. Questions will be answered by Prof. Christof Paar.
https://www.informatik.rub.de/en
http://www.rc-trust.ai/
General note: In case of using gender-assigning attributes we include all those who consider themselves in this gender regardless of their own biological sex.