You are here

PhD Program in Systems Science

PhD Program in Systems Science

Coordinator: Alberto Bemporad

PhD Program Overview

The Doctoral Program in Systems Science aims to provide the necessary methodological skills to analyze technological, natural, economic, and social systems by means of descriptive and predictive mathematical models. Examples of current and relevant problems in modern society that can be treated using such methodologies are ensuring the efficiency of industrial and manufactured production systems, addressing the complexity of economic, biological, ecological and social systems, and making cyber-physical systems, in which software and physical systems interact, autonomous and reliable. These issues play an essential role in many industrial sectors, such as in the automotive, aerospace, chemical, infrastructure, energy, biomedicine, financial, and manufacturing industries.

During the doctoral course, the Ph.D. student will acquire skills to develop, use, and apply methodologies for analyzing and predicting complex phenomena through analytical and computational models derived both from first principles and from data, drawing on interdisciplinary fields such as mathematics, operations research, physics, statistics, computer science, and engineering. More specifically, these skills are acquired by the student within one of the following four specialization tracks:

Each track offers a specialized "vertical" preparation on the corresponding reference, providing a high level of preparation, which is not normally possible to get during undergraduate studies, that is strongly requested for by both academic and non-academic jobs. At the same time, through the attendance of basic courses, candidates belonging to different tracks will be exposed to the techniques and methodologies developed in contiguous, although traditionally not very communicative, disciplinary fields, fully realizing a "horizontal" interdisciplinary training. Overall, the advanced training offered allows students to broaden their range of skills, considerably improving their ability to tackle frontier research problems within their disciplinary field successfully. The student composes his or her study plan by selecting courses from a basket of basic and advanced courses offered by the School, as well as of courses related to soft skills that are useful for the training of a researcher.

The educational offer of the Program includes several topics: machine learning for the reconstruction of models from data to stochastic processes, network analysis, analysis and control of dynamic systems, analysis of time series, numerical optimization, numerical integration of differential equations, statistics, agent-based models, specification languages, programming, and software analysis. In fact, the study of extremely complex systems in terms of size and / or dynamic richness requires mastering an increasingly refined and innovative set of computational techniques. Focusing on this core of general methodological skills provides the student with a shared vocabulary and a wide set of versatile tools to address various application problems of strong industrial and social interest.

Thesis supervisors and course lecturers are internationally recognized experts for their contributions to science and technology transfer. Ph.D. students also can also interact with other institutions and companies that the School already collaborates with.


Input and Output Profiles


Candidates with a master’s degree in computer science, engineering, physics, mathematics, statistics, or related fields can apply to the Program. Career opportunities for students who obtain the Ph.D. in Systems Science are both in academia (engineering, computer science, physics, applied mathematics), and in industry, services, public and private research laboratories, study centers, regulatory centers, consulting firms, and the public sector.

Scientific Board

  • Prof. Alberto Bemporad (Full Professor of Control Systems, IMT School)
  • Prof. Alessandro Abate (Full Professor of Verification and Control, University of Oxford)
  • Prof. Luca Cardelli (Royal Society Research Professor, University of Oxford)
  • Prof. Gabriele Costa (Assistant Professor in Computer Security, IMT School)
  • Prof. Irene Crimaldi (Associate Professor of Statistics, IMT School)
  • Prof. Rocco De Nicola (Full Professor of Computer Science, IMT School)
  • Prof. Moritz Diehl (Professor of Systems, Control and Optimization, University of Freiburg)
  • Prof. Tiziana Di Matteo (Full Professor of Econophysics, King’s College London)
  • Prof. Daniele Dini (Professor in Tribology, Imperial College London)
  • Prof. Diego Garlaschelli (Associate Professor in Theoretical Physics of Matter, IMT School)
  • Prof. Alessio Gizzi (Associate professor in Construction Science, Campus Bio-Medico University of Rome)
  • Prof. Giorgio Stefano Gnecco (Associate Professor in Operations Research, IMT School)
  • Prof. Corrado Maurini (Professor in Mechanics, Sorbonne University)
  • Prof. Marco Paggi (Full Professor of Structural Mechanics, IMT School)
  • Prof. Paolo Prinetto (Full Professor of Data Processing Systems, IMT School and Politecnico di Torino)
  • Prof. José A. Reinoso Cuevas (Associate Professor in Structural Mechanics, Universidad De Sevilla)
  • Prof. Tiziano Squartini (Associate Professor in Theoretical Physics of Matter, IMT School)
  • Prof. Mirco Tribastone (Full Professor of Computer Science, IMT School)
  • Prof. Luca Viganò (Full Professor of Computer Science, King’s College London)
  • Dr. Mario Eduardo Villanueva (Assistant Professor of Systems and Control Engineering, IMT School)
  • Prof. Mario Zanon (Associate Professor in Control Systems, IMT School)