An al-based approach to academic quality quantification

In the context of academic recruitment at universities and research institutions, establish- ing consistent and effective evaluation criteria remains a complex challenge. Identifying robust metrics that align with globally recognized standards of academic quality is essen- tial to ensure merit-based evaluation of researchers and maintain institutional credibility. In this thesis, we address this challenge through an AI-based solution aimed at developing a data-driven approach to quantify academic quality. As a benchmark of academic excellence, we use Nobel laureates' profiles in Physics, Chemistry, Physiology or Medicine, and Economics as a reference cohort. Comparison group includes researchers from the same fields affiliated with universities with an average ranking based on the Times Higher Education World University Rankings. By defining bibliometric features of the academic profiles of both Nobel laureates and the comparison group, we aim to develop machine learning models to quantify academic quality and identify the key features that define academic excellence. The ultimate goal is to support the decision-making process of universities and research institutions in academic recruitment, creating a fair and objective evaluation criteria.