TU DORTMUND - Open Rank: W3 tenured or W2 with W3 T.T. Human-AI Interaction

Your Responsibilities

The person selected to hold the new professorship will specialize in research and teaching in the field of user-centered artificial intelligence. The teaching load is reduced to four weekly hours per semester. The successful candidate is expected to contribute to English-taught courses in Computer Science.

Your Qualifications

We are seeking an outstanding scientist who holds an excellent PhD in Computer Science or related disciplines, has experience in applying for third-party funds and has published in relevant and highly-ranked international venues with peer review. In particular, we welcome applications of candidates who have conducted excellent research in one or more of the following areas:

• Human-AI collaboration
• User control of machine learning
• Auditing intelligent systems
• Design of interactive natural language systems
• AI-assisted software engineering
• User-centric voice and text assistants
• Transparent and explainable artificial intelligence
• AI-aided data exploration and visualization
• Reinforcement learning from human feedback
• Personalization, recommendation and adaptation of algorithms
• Human-Computer interaction for intelligent systems
• Generative AI and content co-creation
• Hybrid intelligence

Applicants should complement the research activities of the RC Trust and the Department of Computer Science and be willing to contribute to interdisciplinary collaborative research projects and collaborations within and outside the RC Trust and the Universities of the Research Alliance Ruhr. Willingness to acquire third-party funds is expected. Potential applications include but are not limited to life sciences, social sciences, health, economics, logistics or engineering. For the W3 professorship, continuous publication activities in relevant and highly ranked international venues with peer review, especially in the recent years, the successful and independent acquisition of research projects or competitive third-party funds, including cooperative joint research collaborations, and a track record in excellent international or interdisciplinary academic collaborations are mandatory.

Strong social and leadership skills and willingness to participate in academic self-administration complete your profile. Moreover, the recruitment requirements in Sections 36 and 37 of the North Rhine-Westphalia Higher Education Act (HG NRW, Gesetz über die Hochschulen des Landes Nordrhein-Westfalen) apply.

In the case of the W2 professorship with W3 tenure track, the successful candidate will initially be appointed as a temporary civil servant for five years. At the end of this five-year period at the latest, the tenure track will lead to continued employment as a tenured W3 university professor, provided that the necessary aptitude, competence and academic performance have been demonstrated and that the legal requirements pertaining to Section 38 of the HG NRW are met.

Attractive Environment

The professorship will be part of the newly established Research Center Trustworthy Data Science and Security. The Center enables research that connects psychology, computer science, statistics and cyber security in order to empower humans to understand, control and verify artificial intelligence technology as well as modern machine learning in (semi-)automated decision making.

Our institute’s language is English.

The Research Center is linked to all three member universities of the University Alliance Ruhr. We offer an excellent scientific environment and attractive opportunities for collaboration with renowned international, national and regional partners. The professorship advertised here will be closely associated with the Departments of Computer Science and Statistics as well as the Dortmund Data Science Center (DoDSc) at TU Dortmund University.

These institutions enjoy an excellent international reputation in the fields of basic and applied machine learning research. These core issues have been consolidated in numerous interdisciplinary research collaborations among more than 30 data science professors. All involved departments have a strong track record of acquiring major extramural support for collaborative research funds in the area of data science and machine learning (SFB 823: Statistical modelling of nonlinear dynamic processes, SFB 876: Providing Information by Resource-Constrained Data Analysis, Lamarr Institute).