Systems Science

PhD Coordinator:  Pietro Pietrini

PhD Program Overview

The PhD Program in Systems Science relies on proficiencies in the development of predictive quantitative models for the analysis of economic, technological and social systems. It deals with increasingly relevant problems that regard, for example, smart grids, social networks, smart communities, smart cities, the management of immigration flows and international exchanges, risk analysis in global financial systems, intelligent and sustainable industrial production systems, health systems, logistics systems and cyber-physical systems, namely systems consisting of the interaction between digital systems and physical units, prominent in automotive, aerospace, chemical, infrastructure, energy, biomedicine and manufacturing industries.
The principal educational objective of the PhD Program is to help students master and effectively employ basic methodological tools (mathematical models, data extraction, statistical analysis, algorithms) and potentially develop new ones, within the specific domains of both its Tracks:

The Program is characterized therefore by the interdisciplinary nature of its innovative approach. Barriers that traditionally divide domains of knowledge are largely overcome through tackling problems that arise in diverse application frameworks - like economics, finance, industry, computer systems, etc. - with a variety of scientific methodologies for the analysis of systems derived from physics, statistics, econometrics, computer science, systems engineering and computational methods. 
In particular, the Program’s educational offering foresees the use of tools such as machine learning and reconstruction of mathematical models from data, stochastic processes, statistics, network analysis, analysis of dynamic systems, numerical optimization methods, numerical integration for differential equations and high-dimensional econometrics, which is increasingly characterized by significant and highly-innovative computational components, that can allow for the study of extremely complex systems by dimension or execution speed, based on tools for the analysis of data, particularly so-called Big Data. Focusing on this core of general methodological skills provides both a broad applicative versatility and a shared vocabulary to deal with various issues.
 
The Systems Science Program aims at enriching the educational program of the Computer Science and Systems Engineering Track by integrating field-specific training with the capability to analyze the socio-economic dimension of problems, in terms of financial aspects, decision and game theory, and the analysis of institutional, regulatory and patenting contexts. On the other hand, students of the Economics, Networks and Business Analytics Track will gain more in-depth knowledge of linear algebra tools, numerical methods for differential equations, optimization, programming and control of dynamic systems, network analysis, statistics and machine learning, and the management of large databases. 

Input and Output Profiles

Perspective students should preferably have a master-level background in computer science, engineering, economics physics, mathematics, statistics or in a related field.

The program offers a preparation to analyze and resolve a broad spectrum of highly complex problems with an elevated institutional, social and industrial interest, with the primary aim of identifying government solutions and effective intervention policies in different domains. Employment opportunities are therefore found both within the academic realm, in various disciplines (engineering, computer science, economics and management), and in the public sector, in research laboratories, study centers and regulatory centers, as well as in the private sector (services, industry and professional consultancies). 

Research Units contributing to the PhD Program

 AXES,   DySCO,   MoMiLab,   MUSAM,   Networks,   SysMA.
Ph.D. students also have the opportunity to collaborate with other institutions that work with IMT Research Units.

Coursework