W3-University Professorship (f/m/d) Scientific Machine Learning (Heinz Nixdorf - Endowed Professorship)

Paderborn University is a high-performance and internationally oriented university. Within interdisciplinary teams, we undertake forward-looking research, design innovative teaching concepts and actively transfer knowledge into society. As an important research and cooperation partner, the university also shapes regional development strategies. We offer our employees in research, teaching, technology and administration a lively, family-friendly, equal opportunity environment, a lean management structure and di-verse opportunities. Join us to invent the future!  

The Faculty of Computer Science, Electrical Engineering and Mathematics at Paderborn University is seeking to fill the following position at the Department of Computer Science as soon as possible:

W3-University Professorship (f/m/d) Scientific Machine Learning (Heinz Nixdorf - Endowed Professorship)

The professorship will play a connecting role between the research areas of High-Performance Computing, Machine Learning (ML)/Artificial Intelligence (AI), and Computational Science and Engineering. It will strengthen cooperation with the interdisciplinary scientific institutions - the Paderborn Center for Parallel Computing (PC2), the Institute for Photonic Quantum Systems (PhoQS) and the Heinz Nixdorf Institute (HNI) - as well as with the participating faculties 'Computer Science, Electrical Engineering and Mathematics' and 'Natural Sciences'. The establishment of the new professorship is funded by the Heinz Nixdorf Foundation. 

We are looking for a person with outstanding international scientific credentials who can represent the research field of Scientific Machine Learning (SciML) in all its breadth. The focus should be on innovative approaches that combine traditional numerical simulation methods with modern data-driven methods in order to be able to work on complex scientific issues. Particular emphasis is placed on sound practical experience in the application of SciML methods in real fields of application and on high-performance computers. 

Outstanding scientific achievements are expected, e.g. in the following areas: 

• Design, analysis and optimization of scientific machine learning methods 
• Practical development of SciML methods on modern, highly scalable computer systems, in particular using hardware accelerators 
• Use of SciML methods for modeling complex systems, in particular to improve the relevance and realism of scientific simulations 
• Development of methods for the combination of quantum computers and classical computers, e.g. for quantum machine learning 

The field of work should be closely linked to current research priorities at the university through the practical application of SciML methods in natural or engineering sciences, e.g: 

• Photonics, optoelectronics and quantum systems 
• Intelligent cyber-physical systems 
• Energy technology and sustainability 
• Materials and material 

In teaching, proven teaching competence and didactic skills, appropriate participation in compulsory courses, and expansion of the range of basic and advanced courses in Scientific Machine Learning and Scientific Computing for the Computer Science and Computer Engineering programs. In addition, we expect a commitment to the further development of the degree programs or the establishment of new specializations or degree programs to strengthen the university's profile in the fields of computational science and machine learning. 

For a successful application, success in acquiring competitive third-party funding is expected commensurate with the academic age and previous environment. 

The participation in academic self-administration, teaching in German and English, and the development of digital teaching formats are expected. Applicants who are unable to teach in German at the time of appointment are expected to acquire the necessary language skills within two years and then also teach selected courses in German. 

We offer an attractive and internationally visible research environment with access to excellent high-performance computers and quantum computing resources. The professorship will be centrally involved in the application for large-scale projects and in existing research facilities (PC2, PhoQS, HNI). The explicit focus on highly scalable applications and the locally available supercomputer infrastructure provide an ideal environment for the professorship to gain and further develop practical experience with the implementation of SciML methods in real large-scale scientific projects. Thanks to funding from the Heinz Nixdorf Foundation, the professorship has access to attractive staffing, ongoing funding and initial equipment.