IN PISA A NEW DOCTORAL TRAINING PROGRAM IN DATA SCIENCE WILL SUPPORT INTERDISCIPLINARY RESEARCH AND STIMULATE ECONOMIC GROWTH AND EMPLOYMENT
On June 16, Scuola Normale Superiore, Scuola Superiore Sant’Anna, Università di Pisa, Scuola IMT Alti Studi Lucca and CNR (Italian National Research Council) have announced their new joint PhD program in Data Science. The doctoral program, which will begin on 1 November 2017, covers a wide variety of topics in statistics, programming, data analysis, machine learning, and neural computation. This PhD program in data science will support interdisciplinary research and teaching several academic disciplines. Data scientists are much in demand: all companies in all industries, governments and society are increasingly adopting data science and applications of big data analytics.
The PhD program is open to students worldwide. The five partner institutions, Scuola Normale Superiore, Scuola Superiore Sant’Anna, Università di Pisa, Scuola IMT Alti Studi Lucca and CNR, offer 8 PhD scholarships for the academic year 2017/2018. The PhD program in Data Science is a 3-year full-time program fully funded. The students of the joint doctoral training will be “normalisti” students. At the end of the program, students will attain the title of Philosophiæ Doctor (PhD) in “Data Science”, jointly awarded by the Director of the Scuola Normale, the Rector of the University of Pisa, the Rector of the Scuola Superiore Sant’Anna and the Director of the Scuola IMT.
The five partner institutions, based in Pisa and Lucca, will train a new generation of data scientists, with the “big data” skills and interdisciplinary awareness. According to the World Economic Forum the number of job postings for data scientist will rise by 15 percent from their current levels by the end of the decade. As data science becomes increasingly influential, data scientists will need to be aware of how data science, communications, economics, law and policy makers can work in partnership.
The PhD program will train data science professionals to collaborate on data-driven research to address major challenges. Companies will require the expertise of skilled machine learning specialists, programmers and data optimization managers who will focus on collating and interpreting key data.
