Scientific Director of the AI Methods & Software Hub

The AI Methods and Software Hub for "Machine Learning in the Sciences" acts as a central interface between the Cluster of Excellence and the university's departments. It pools expertise in applied machine learning and the development and long-term use of research software. The scientific director will establish a project-based consulting structure to facilitate knowledge transfer from the Cluster of Excellence to other departments at the university and foster collaborations between machine learning and application domains. Furthermore, the director will establish structures that enable broader adoption of software packages. These software packages should be maintained long-term to ensure they are easily usable by the broader AI and machine learning community beyond Tübingen., * Machine learning consulting for scientific projects from a range of disciplines.

• Development of prototypes for standard use cases.
• Organization of networking events between the ML department and scientific application subjects.

Research software infrastructure:

• Selection of software packages for maintenance.
• Licensing for open-source projects.
• Dissemination on appropriate platforms such as github.

Leadership and Training:

• Personnel responsibility for the AI Methods and Software Hub.
• Development of courses for introducing doctoral students and scientists from other disciplines to machine learning. Teaching in bachelor's or master's programs and transfer to industry is explicitly not required.
• We are looking for candidates with experience supporting scientific projects at the interface of machine learning and other scientific disciplines, and with expertise in structuring and maintaining research software.
• The position offers options for creative developments in a vibrant environment. You will have the opportunity to contribute to publications and third-party funding applications, although this is not the primary goal of the position.

• A PhD in machine learning or data science and a background in computer science, physics, mathematics, or related subjects.
• In-depth knowledge of scientific software development.
• Good communication skills.
• Programming experience in common scientific programming languages (Python, Julia, R, C++, etc.).
• Experience in project management.
• Curiosity about research in different scientific fields, and openness to familiarize yourself with new application domains.