Professor of Data Analysis 2

University of Graz; Graz; Vor Ort; EUR 90.000 - 120.000; Vollzeit

Zusammenfassung

University of Graz invites applications for a professorship focusing on natural language processing within the interdisciplinary context of a comprehensive university. The role emphasizes building robust research networks, advancing foundational methods, and integrating NLP with multiple disciplines across the university.

The appointed professor will strengthen the IDea_Lab’s methodological expertise, foster cross‑faculty collaboration, and contribute to teaching at all levels.

Qualifikationen

• Excellent knowledge of English and willingness to learn German.
• Leadership and management experience in an academic setting.
• Track record in attracting research grants and building interdisciplinary teams.

Aufgaben

• Strengthen methodological expertise within the IDea_Lab across the University of Graz.
• Promote interdisciplinary NLP research and teaching in collaboration with faculties.
• Lead and mentor early career researchers and supervise research projects.

Kenntnisse

Excellence English

Leadership

Ausbildung

Doctorate/PhD

Habilitation

Jobbeschreibung

Scientific Profile

The professorship focuses on the fundamentals and applications of natural language processing (NLP) within the interdisciplinary context of a comprehensive university. A key aspect of digitalisation is the ever‑growing volume of data, which is often unstructured – for example, in the form of natural language. Linked to this are fundamental methodological questions in natural language processing that support a wide variety of approaches to solve tasks in the humanities, natural sciences, law, social sciences, business and economics as well as environmental and educational sciences.

Suitable applicants

Suitable applicants will have used NLP approaches to solve tasks such as:

• information extraction, information retrieval, and text mining
• retrieval‑augmented generation (RAG)
• sentiment‑ and emotion analysis
• argument mining, discourse and pragmatics
• machine translation and summarisation
• symbolic reasoning

At the same time, suitable applicants should have a sound foundation in the development of fundamental methods, for example:

• language modelling
• efficiency
• model alignment
• bias, fairness, and explainable AI (XAI)
• evaluation and benchmarking

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 computational social science, data science and applied mathematics as well as the various subject areas 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 natural language processing across the entire University of Graz and to represent this area in teaching. The appointed professor is expected to have a strong intrinsic interest in building robust interdisciplinary research networks within the University of Graz. Building on the inter‑faculty IDea_Lab, the research field is to be further consolidated through innovative interdisciplinary concepts and research approaches, in collaboration with the professorships already based at the IDea_Lab as well as research partners in the individual faculties and departments.

Employment Requirements

• Austrian or equivalent foreign higher education degree corresponding with the position (doctorate/PhD)
• Habilitation or equivalent qualification in computer science, computational linguistics 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 natural language processing
• Experience and expertise in interdisciplinary collaboration, proven by quality‑assured publications in the respective application domain
• Experience and/or willingness to engage in interdisciplinary teaching
• Experience in science communication (preferable)
• Experience in open science (preferable)

Selection Criteria

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.

Application Documents

• Letter of application
• Curriculum vitae with a description of your academic career
• 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 with a short statement describing your specific contribution and the scholarly significance of each publication
• Leadership concept, including gender mainstreaming strategy

Please submit your application documents in English.

Equality Principle

The University of Graz is committed to increasing the proportion of female employees, especially in leadership roles. We therefore encourage qualified female colleagues in particular to apply for this position. In case of equal qualifications, women will receive priority consideration.

Please note that in order to comply with the applicable data protection regulations, we can only accept applications via our web‑based applicant tool for this vacant position.

Hearing Dates

Planned dates of the applicant's presentation: February 24-26, 2027

Contact

Univ.-Prof. Dr. Jana Lasser, [e-mail]