The AI-Powered Academic Talent Finder

Academic recruitment is a complex process demanding evaluation of scholarly excellence as much as pedagogic qualifications. Traditional methods are typically founded on fragmented web searches, non-uniform CV templates, and time-consuming manual examination, leading to inefficiencies which conceal highly deserving candidates. In today's competitive higher education environment, universities must be capable of rapidly spotting and examining best talent to achieve global competitiveness as much as academic superiority.



The AI-Powered Academic Talent Finder addresses this challenge by providing an end-to-end, automatic solution for academic candidate identification and evaluation. The system, with its advanced natural language processing and machine learning, mines and cleans data from uploaded CVs, collecting both research impact metrics and teaching experience. The filtered list of data is then ranked and analyzed against institutional needs, enabling recruiters to shortlist, compare, and filter more precise and transparent candidates. An intuitive dashboard offers actionable insights reflecting candidate-recruitment priority match. Besides efficiency, the system also enhances fairness by reducing human bias in candidate evaluation. This project presents an innovative step toward making higher education university hiring a fair, data-driven, and efficient process for informed decision-making.