Statistical methods for high dimentional data 2026_RTT_DMAT_1

The increasing availability of complex, heterogeneous, and high-dimensional data generated by digital technologies, sensors, and online platforms poses new methodological and computational challenges across a wide range of application domains, including healthcare, engineering, industry, environmental sciences, and social sciences. The research will focus on developing advanced statistical methods for the analysis of complex data, including functional data, non-Euclidean data, object data, and data exhibiting spatial, temporal, or spatio-temporal dependence. The research will integrate theoretical aspects, including the development of novel statistical models, inferential procedures, and computational algorithms, with applied research aimed at developing and validating these methodologies on real-world problems. The candidate will be involved in teaching and tutoring activities in subjects related to the field of applied statistics.