TU Dresden is one of the top universities in Germany and Europe and one of the eleven German universities with the status of a university of excellence. As part of its strategic development, TU Dresden strives for strong interdisciplinary networking by expanding computational science approaches across different disciplines. To this end, the Center for Interdisciplinary Digital Sciences (CIDS) holistically and synergistically brings together Dresden’s research on digitization in all scientific fields as a focal point for innovation and interdisciplinarity. Another component is the newly acquired NHR center “HPC-Data Analytics II” by the ZIH of TU Dresden for the provision of fast computing power, which will be funded with a total of more than 80 million euros over the next 10 years. The hardware will include CPU and accelerator architectures designed especially for innovative AI applications. Become part of it and work together with 600 employees from various departments under one roof in the new Lehmann Center on issues related to the digital sciences of the future.

The BMBF competence center ScaDS.AI Dresden/Leipzig Center for Scalable Data Analytics and Artificial Intelligence, as important department of the CIDS for the strategic development of the fields of data analytics and artificial intelligence, is to bring together more than 150 employees at the Dresden site alone in the medium term. For this purpose, the Faculty of Computer Science invites applications for

four Strategic Chairs
in the thematic field of Data Science and Artificial Intelligence

to be filled at the next possible date.

These chairs will play a central role in ScaDS.AI. In order to close the gap between the efficient use of mass data (Big Data), advanced AI methods and knowledge representation, a total of eight new chairs in the fields of Data Analytics and Artificial Intelligence will be established at both ScaDS.AI locations (Dresden and Leipzig). Thus, the already established methodological focus on Big Data will be further developed in the direction of innovative approaches and procedures in artificial intelligence (AI). At TU Dresden, fundamental research areas will be established through the new chairs “Data Sciences”, “Scalable Software Architectures for Data Analytics”, “Knowledge-based Artificial Intelligence” and “Machine Learning for Spatial Understanding”. This will also advance the use of AI methods in various application areas.

The positions offer an excellent environment within the national competence center ScaDS.AI Dresden/Leipzig, which is funded by the BMBF and the Free State of Saxony. This includes the opportunity for interdisciplinary collaboration with computer scientists, mathematicians, natural scientists, as well as scientists from the life sciences, medicine, environmental sciences, earth system sciences and engineering. There is access to state-of-the-art technologies and an excellent high-performance computing infrastructure. Further information on the ScaDS.AI focal points can be found at

Chairs are usually appointed as W2 positions. An upgrade to a W3 position may be considered if the ScaDS.AI Dresden/Leipzig excellence criteria are exceptionally fulfilled: outstanding research results, proven success in mentoring young scientists, high international visibility, coverage of a broader research area and innovative and preferably interdisciplinary research approaches. The four strategic chairs are being advertised with the following main topics:

Chair (W2/W3) of Data Science

The chair aims to contribute to deepening the connection between data science and AI by developing new algorithmic methods. Emphasis is placed on new methodological developments that are inspired by and have practical relevance to concrete applications. Own contributions are expected, for example, on one or more of the following topics: Algorithms for data-driven modeling or simulation of complex systems; methods of model inference from data or model learning; methods for model-free prediction from data; algorithms for high-dimensional data spaces; optimization or design-centering algorithms for machine learning; dimensionality reduction methods or data embedding methods; data interpretation methods or data visualistics; uncertainty analysis methods; co-design of data analysis methods and machine learning methods, and data engineering and active learning. More information about the respective chair can be found at:

Chair (W2/W3) of Scalable Software Architectures for Data Analytics

The chair is intended to contribute to extending methods for efficient and scalable software architectures in the field of data analytics, in particular machine learning, in order to solve the challenges resulting from the processing of very large and heterogeneous data. The focus is on aspects such as scalability and increased efficiency. The methods considered may include recurrent analysis of the same data in the context of model optimization in certain learning procedures as well as the analysis of very large data sets. Scaling learning methods are only possible if both the algorithmic aspects of the analysis and the properties of the necessary data operations are considered holistically, related to the context of the underlying computer architecture, and incorporated into the software architecture. Furthermore, novel methods for the efficient interaction between the central systems for the learning process and peripheral systems for the application of the learned systems are necessary (edge/cloud integration). This requires communication methods that can handle changing latencies. More information about the respective chair can be found at:

Chair (W2/W3) of Knowledge-based Artificial Intelligence

The research focus of the chair shall be on knowledge-based, primarily symbolic AI methods and their application in intelligent systems. Relevant research areas of the chair include declarative problem solving, logical reasoning, algorithmic search and optimization, machine learning on structured data, and heuristic methods. Special emphasis is put on the connection of these methodological foundations with application-oriented research up to the development of concrete systems. This should result in practically relevant contributions to at least one AI application area, for example in the area of language processing, knowledge graphs, planning and scheduling, knowledge-based systems or intelligent agents. More information about the respective chair can be found at:

Chair (W2/W3) of Machine Learning for Spatial Understanding

The chair aims to contribute to the development of robust, efficient and scalable methods for automatic understanding of three-dimensional structures, scenes and objects using machine learning methods. We are particularly interested in the following research areas and topics as well as in their application in the fields of autonomous driving, industrial automation, robotics, and medical diagnostics and intervention: Efficient machine learning for point cloud and depth map analysis; simultaneous localization and mapping (SLAM); machine learning for deformable object recognition; multimodal spatial reconstruction, especially with light, radar, and ultrasound; robust spatial reconstruction by sensor fusion; semantic scene understanding; adaptive spatial reconstruction and robot navigation. More information about the respective chair can be found at:

In case of further questions, please do not hesitate to contact the Dean of the Faculty of Computer Science, Prof. Dr. Ivo F. Sbalzarini, phone +49 351 463-32815; e-mail:, as well as the director of ScaDS.AI, Prof. Dr. Wolfgang E. Nagel, phone +49 351 463-35450; e-mail:

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