Professor of Machine Learning Methods 2

The professorship focuseson the methodological foundations and applications of probabilistic machine learning methods in the interdisciplinary context of a comprehensive university. Probabilistic models and probabilistic machine learningmethods are key building blocksof modern AI and constitute a fundamental basis for the application of AI in the humanities, natural sciences, environmental sciences, law, social sciences, and economics.

Suitable candidates will, for example, address topics such as

• the development and analysis of probabilistic models (e.g. Bayesianmodels, latent variablemodels),
• efficient inference methods(e.g. sampling methods),
• uncertainty quantification, calibration, and probabilistic forecasting, as well as the interdisciplinary application of these methods.

The professorship is embedded in the IDea_Lab of the University of Graz, which is the recently established Interdisciplinary Digital Lab for foundational and application-driven research in machine learning, data science and digital transformation. The professorship maintains close ties to statistics, optimisation, data science, applied mathematics, as well as the various specialised fields of the university.

The position is explicitly intended to strengthen the methodological expertise of the IDea_Lab, to promote interdisciplinary research in the field of probabilistic machine learning across the entire University of Graz, and to be responsible for this area in teaching. The appointed professor is expected to have a strong intrinsic interest in building robust interdisciplinary research networks withinthe University of Graz, startingfrom the inter-faculty IDea_Lab, and in consolidating the field - together with the professorships already based at the

IDea_Lab and research partners in the individual faculties and departments - through novel interdisciplinary concepts and research approaches., * List of publications, numbered, with a complete bibliographic information, sorted by

• monographs
• (co-)edited volumes
• journal articles
• other information
• Teaching statement including presentation of teaching to date
• List of previous research projects and collaborations
• Description of future research intentions
• List of your five most important publications

Please submit your application documents in English.

Hearings

Planned dates of the applicant`s presentation: March 1-3, 2027

• Austrian or equivalent foreign higher education degree corresponding with the position (doctorate/PhD)
• Habilitation or equivalent qualification in Mathematics, Computer Science or related disciplines
• 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)
• Success in attracting subject-specific project grants, particularly where competitively awarded third-party funds are concerned
• Skills in higher education didactics including the use of digital media
• Skills in the supervision and guidance of early career researchers
• Professional experience abroad during academic career
• Management and leadership experience
• Gender mainstreaming and diversity management skills
• Excellent knowledge of English and willingness to learn German
• Outstanding scientific achievements in both the interdisciplinary applications and the methodological development of probabilistic machine learning
• Experience and expertise in interdisciplinary collaboration, demonstrated by relevant publications in high-ranking journals of the respective application domain
• Experience and/or and willingness for interdisciplinary teaching
• Experience in science communication (preferable)
• Experience in open science (preferable)

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.

We offer a diverse, challenging, team-oriented working environment and a high degree of personal responsibility. Working hours are flexible and there are many options for further education and personal development.

The minimum remuneration as stated in the Collective Bargaining Agreement is € 93,986.20 gross per year. Salary subject to negotiation.

At the University of Graz, researchers and students work across a broad disciplinary spectrum to enlarge our knowledge, and find strategies to deal with challenges our society is confronted with and to shape tomorrow’s world. The University of Graz is a place which combines high quality academic research and teaching, where achievement is rewarded, careers are promoted, and social diversity is encouraged - all within a modern, award-winning working environment. Our motto: We work for tomorrow. Join us!