Director: Mirco Tribastone
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 require a novel holistic interdisciplinary approach, combining fundamental research at least in the following domains: synthesis and verification of highly concurrent computing systems; machine-learning and numerical optimization for data-driven modeling and control of dynamical systems; modeling and simulation of smart interfaces and materials for advanced sensing and energy harvesting; analysis of massive quantities of data, such as imaging data.
The CSSE track 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, cybersecurity, 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:
- Research in computer science deals with the development of languages, models, algorithms, and verification methods for modern distributed systems. In particular, the research focuses on cutting- edge investigations of adaptive systems, automated verification, cloud computing, cybersecurity, dynamical systems, mobile systems, and performance evaluation.
- Research in control systems covers machine learning techniques for identification of dynamical models from data, and optimization-based control of dynamical systems with an emphasis on real- time embedded optimization algorithms for model predictive control of small-scale/fast, stochastic, distributed, and large-scale dynamical systems. Application areas include industrial problems arising from the automotive, aerospace, smart-grid, and finance domains.
- Research 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.
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 track 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 the private sector.
Research Units contributing to the track
Ph.D. students also have the opportunity to collaborate with other institutions that work with those Research Units.