Filipe R. Ramos is Associate Professor at the Faculty of Social Sciences and Technology, Universidade Europeia (FCST-UE), where he also holds academic coordination roles in degree programmes, postgraduate programmes and executive education. He is also Invited Professor at the Faculty of Sciences, University of Lisbon (FCUL).

He holds a Ph.D. in Management (specialisation in Quantitative Methods) from ISCTE Business School, ISCTE – Instituto Universitário de Lisboa (ISCTE-IUL). He also holds a Master’s degree in Financial Mathematics from ISCTE-IUL and FCUL, as well as a Master’s degree in Mathematics Teaching from NOVA School of Science and Technology, NOVA University of Lisbon (NOVA FCT). He graduated in Mathematics (Teaching) from FCUL. His pedagogical development also includes postgraduate training in Digital Education and in Pedagogical Innovation in higher education.

His teaching experience spans several levels of education, including the teaching of Mathematics in primary and secondary schools, Applied Mathematics in vocational education programmes, and courses in Mathematics and Statistics at undergraduate, master’s, doctoral and executive education levels in several public higher education institutions, including FCUL, ISEG – Lisbon School of Economics and Management, NOVA FCT and ISCTE-IUL, as well as in private higher education institutions (Universidade Europeia). In addition to teaching, he has served as a technical-pedagogical collaborator and consultant at IAVE and as coordinator of vocational education programmes and their pedagogical teams.

His research focuses on data analysis and mathematical modelling, particularly in time series analysis and forecasting, data science, and machine learning and deep learning, with applications in economics, management and finance. He is an integrated researcher at CETRAD – Europeia Hub and a collaborating researcher at CEAUL and LAETA. He is currently the Principal Investigator (PI) of the research project “Mapping Volatility Trends in the Cryptocurrency Market: Hybrid Artificial Intelligence Models Integrating Machine Learning and Deep Learning”, funded by Europeia-ID. The research work has been published in indexed international journals and conference proceedings and is indexed in Web of Science, Scopus and Google Scholar, contributing to the advancement of quantitative methods and data science applications in economics and business research. His current research interests also include hybrid artificial intelligence models, financial time series modelling and applications of data science to economic and business contexts.