Tenure Track Professor of Mathematical Foundations of Machine Learning

Tenure Track Professor of Mathematical Foundations of Machine Learning

University of Graz - Department of Mathematics and Scientific Computing

Graz, Austria

Job Description

Initially the successful candidate starts as an Assistant Professor with a qualification agreement for a fixed term tenure phase of six years. After successful completion of the qualification goals, the position will be converted into a permanent Associate Professor position.

The Department is looking for a person of a high international standing who represents the mathematical foundations of machine learning in research and teaching. The candidate is expected to be compatible with the existing research profile of the Department; the participation in or collaboration with one of the research networks/institutions COLIBRI, BioHealth, BioTechMed, IdeaLab, NAWI-Graz, or Special Research Programme MR-DYNAMO is preferred.

Employment Requirements

• Austrian or equivalent foreign higher education degree corresponding with the position (doctorate/PhD) in the field of Mathematics;
• Outstanding academic qualifications in research and teaching in the relevant discipline and for the profile of the professorship (commensurate with stage of academic career and interruptions in employment due to caring responsibilities);
• Several years of experience as post-doc or in a similar position;
• Skills in attracting subject-specific project grants;
• Skills in higher education didactics including the use of digital media;
• Gender mainstreaming and diversity management skills;
• Professional experience abroad during academic career is preferred;
• Very good knowledge of English;
• Ability to teach in German (after a transitional period of preferably two years);
• International experience in presenting papers and delivering lectures;
• Experience in international networking within the scientific community.

The successful candidate will be highly motivated, aiming for academic excellence and integrity in research and teaching. He/she will have demonstrated ability to collaborate constructively in a responsible manner and inspire colleagues and students in an interdisciplinary, internationally oriented context.