The IDea_Lab of the University of Graz is seeking to appoint a
Professor of Data Analysis

(40 hours per week; selection procedure in accordance with Section 98 of the Universities Act (UG); permanent employment according to the Austrian Law on Salaried Employment (AngG); expected starting date March 1st 2024)

A central aspect of digitalization in terms of both research and everyday life are steadily increasing amounts of data (Big Data). This entails methodological questions such as the identification of basic parameters (feature engineering), the quantification of uncertainties, the discovery of patterns and causalities, the appropriate explanation of solutions (e.g., by means of Interpretable AI, or Explainable AI), as well as interdisciplinary applications ( e.g, in the humanities, natural sciences, law, social sciences or economics. The professorship deals with the foundations of analysis methods and models in data science, focusing on Big Data in an interdisciplinary context within a regular university and in coordination with the professorship “Machine Learning Methods”. Suitable candidates deal with topics such as feature engineering in large amounts of data, the discovery of causal relationships, the analysis and quantification of uncertainties in data-based predictions and decisions, as well as deterministic, statistical or Bayesian models. The professorship is intended to strengthen the methodological competence of the IDea_Lab and promote interdisciplinary research in the field of Data Analysis within the entire University of Graz and to represent it in teaching. Starting from the cross-faculty IDea_Lab, the to-be-appointed professor has an intrinsic interest in networking the field within the University of Graz, using excellent communication skills, and in establishing the field jointly with the professorship Machine Learning Methods as well as research partners at the different faculties/departments, using novel interdisciplinary concepts and research approaches.

For more information please follow the link: here.