Computer Science and Systems Engineering

Director: Rocco De Nicola

Curriculum overview

Current trends in society show an increasing pervasiveness of information and communication technologies into our lives, as witnessed by the growing popularity of mobile, portable, and wearable devices, as well as by the massive shift toward equipping everyday objects with computational and networking capabilities.

The integration of computing devices and physical processes leads to the emergence of new cyber-physical systems that exhibit intricate dependencies between parts of inherently different nature. These systems pose very challenging and fundamental questions of both methodological and technological nature. Their successful engineering and operation requires a novel holistic inter-disciplinary approach, combining fundamental research at least in the following domains: synthesis and verification of highly concurrent computing systems; data-driven modeling, control and optimization of large-scale dynamical systems; modeling of novel surfaces and materials; analysis of massive quantities of data, such as imaging data.

The CSSE curriculum provides the doctoral student with a solid interdisciplinary background to analyze cyber-physical systems and provide solutions to a huge variety of complex engineering problems. The program of studies is based on a set of common courses, covering the fundamentals of numerical linear algebra and numerical methods for differential equations, computer programming, dynamical systems and control, numerical optimization, stochastic processes, and machine learning. These basic courses are followed by a number of advanced courses and research seminars, related to the different areas of specialization for the PhD work, that can be chosen by the student.

The specialization in computer science deals with the development of models, algorithms, and verification methods for modern distributed systems, focusing on cutting-edge research issues on the fundamentals and the applications of architectures and languages to modern distributed systems, including global and cloud computing systems, web systems and services, and mobile systems.

The specialization in control systems is oriented towards identification and optimization-based control of dynamical systems, with an emphasis on real-time embedded optimization algorithms for control of stochastic, distributed, and large-scale dynamical systems, and their application in industrial problems arising from the automotive, aerospace, and smart-grid domains.

The specialization in computational mechanics is concerned with the development of innovative computational methods to study advanced problems of solid mechanics, fluid mechanics, and cutting-edge problems involving multiple fields and length scales of high interest in both the academic and industrial sectors.

Students focusing on image analysis work on devising new machine learning and data mining algorithms for innovative feature extraction, sparse data representation and scientific visualization of large-scale multimodal data, with emphasis on images arising in the natural and life sciences.

Input and Output Profiles

Perspective students should preferably have a master-level background in computer science, engineering, physics, mathematics, statistics, or in a related field. The CSSE curriculum prepares researchers and professionals that are able to analyze and propose constructive solutions to several real-life problems of industrial, economic, and societal interest, making them qualified to work in high-profile professional roles within universities, research centers, and companies.

Research Units contributing to the curriculum


Ph.D. candidates also have the opportunity to collaborate with other institutions that work with IMT Research Units.


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