W3 Associate/Full Professor in the fields of Robotics and Artificial Intelligence (m/f/d)

With the foundation of the University of Technology Nuremberg (UTN) on January 1st, 2021, the State of Bavaria paved the way for a new and unique kind of university in Germany:  interconnected with industry and society, open to the public, and on a fully digitalized campus. The UTN has an international profile and a research and teaching philosophy where engineering, natural science, and liberal arts are thought and taught together. A sustainable campus covering roughly 37.5 hectares is currently under construction for a total of 6,000 students in the medium term.

The members of the UTN are convinced that the greatest potential for innovation arises when excellent minds meet at the boundaries of their research fields. For this reason, the UTN comprises truly interdisciplinary departments and their members will perform research and teaching even across different departments. Besides the newly-founded Department of Engineering where a major topic will be robotics and artificial intelligence, a second department Liberal Arts and Sciences is currently being founded.

The University of Technology Nuremberg is looking to fill up to 3 positions as

W3 Associate/Full Professor in the fields of Robotics and Artificial Intelligence (m/f/d)

at the earliest possible date.

The University of Technology Nuremberg seeks outstanding candidates representing the field of robotics and artificial intelligence in research and teaching. Up to three positions may be filled. The major research focus of the candidates should be in one or multiple areas of computer vision (with contributions to multiple areas including pose estimation, activity recognition, neural rendering, vision and language, image/video captioning, image/video question answering, generative modeling, reconstruction, video understanding, segmentation, and tracking), data analytics (with contributions to big data analysis, data mining, statistical data analysis, large data management, active learning, and learning infrastructures) or machine learning (with contributions to deep learning, supervised, unsupervised, as well as self-supervised learning, reinforcement learning, representation learning, uncertainty estimation, explainability, causality, and neural architecture search). Applicants are expected to demonstrate the application potential of their research as well as the willingness to contribute to establishing the growing research area of artificial intelligence and robotics at the Department of Engineering at the Technical University Nuremberg.


A university degree and an outstanding doctoral degree or equivalent scientific qualification, appropriate to the corresponding career level (see further https://www.utn.de/en/professorships_and_appointments/), as well as pedagogical aptitude, are prerequisites. Proven interdisciplinary research and teaching experience are desired. The UTN expects participation in all teaching activities at the Department of Engineering, including study course planning and engagement in the development of innovative digital teaching and learning concepts, as well as cooperation within and across departments.

The UTN is an equal opportunities employer and considers diversity an asset. The position is suitable for severely disabled persons. Severely disabled applicants will be given preference if their suitability, qualifications and professional performance are otherwise essentially equal. Women are encouraged to apply following Art. 7 (3) of the Bavarian Equal Opportunities Act.


Please apply by submitting your application by e-mail to appointments@utn.de (letter of motivation, CV including third-party funding and awards, list of publications, proof of teaching experience and teaching evaluations, university and high school diploma) as one pdf-file by May 25th, 2022, stating as reference PF-2022-02.


Please contact us at appointments@utn.de for any questions regarding the appointment process. For questions regarding the scientific profile of the position, please contact the Department Chair Prof. Dr. Wolfram Burgard, wolfram.burgard@utn.de.

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