For this competitively equipped professorship, an outstanding, internationally visible research profile and the substantial acquisition of competitive third-party funds are expected. The position is intended to bridge machine learning and practical computer science. Research therefore should focus on machine learning centered around real-world applications in one of the following areas:
- Distributed or federated intelligent systems and algorithms
- Machine Learning Systems
- Reinforcement Learning
- Computer Vision
- Natural Language Processing
In teaching, the professorship is involved in all degree programs offered by the department, including cognitive sciences and the new master's program in machine learning. Participation in the academic self-government of the university and the department of computer science is expected.
Required qualifications include a PhD or equivalent international degree and postdoctoral qualifications equivalent to the requirements for tenure. This includes evidence of teaching effectiveness.
The University of Tübingen is particularly interested in increasing the number of women in research and teaching and therefore strongly encourages women candidates to apply. In line with its internationalization agenda, the university welcomes applications from researchers outside Germany. Applications from equally qualified candidates with disabilities will be given preference.
Applications with supporting documents (cover letter, Curriculum Vitae, list of publications and teaching experience, teaching statement, research statement (with intended collaboration), diplomas/certificates) and the completed “application form” (https://uni-tuebingen.de/en/faculties/faculty-of-science/faculty/service-downloads/#c608746) should be sent by e-mail as a single PDF-file (max 10 MB) to the Deputy Dean, Prof. Dr. József Fortágh, of the Faculty of Science, University of Tübingen, Germany (firstname.lastname@example.org). The closing date for applications is March 12, 2021. Enquiries may also be directed to this address.