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2nd year

Advanced Mathematical Methods

Objectives: training students from different disciplines, such as applied mathematics, physics, engineering, to integrate theory and models in the study of some problems arising in applied sciences and which result in partial differential equation. Provide students with a mathematical background suitable to analyze them.

Control Theory

Objective: providing advanced notions of the theory of dynamical systems both in continuous and in discrete time. Introduce to modern techniques for the design of complex control systems with particular reference to application contexts of engineering interest in the industrial field.


Dynamical systems

Objective: providing the foundations of the modern approach to the control of dynamical systems, with particular reference to the treatment of uncertainty, structured and unstructured. Provide the main tools and methods for the analysis and synthesis of multiple-input-multiple-output control systems.

Optimization Models

Objective: providing students with the methodological, theoretical and practical tools to formulate linear programming models and combinatorial optimization problems and to solve them, even for high dimensionality problems, using appropriate optimization software.

Cyber-Physical Systems

Objective: introduces the students to the design and analysis of Cyber-Physical Systems, we will see how to model such systems, how to specify and monitor their behaviors using formal languages as temporal logics, and how to use monitoring techniques for different applications as parameter synthesis and falsification test 

Open Data Management and the Cloud

Objective: providing students with practical information on how to design data models and data structures, to manage metadata to optimize access and research, and to become familiar with interoperability standards. The course will focus on the concept of open data, with efficiency for big data projects, and the concept of cloud as an infrastructure for data management and their processes.

Bioinformatics and Genomic Data Analytics

Genome sequencing technologies have revolutionised our approach to study biological systems. From healthy tissues to diseases, many questions are approached through the collection of far larger sequencing datasets, and some argue that all modern biology is computational biology. Sequencing technologies can measure molecular states at various degrees of resolution and noise, and complex bioinformatics/ machine learning pipelines are required to extract complex patterns that characterise from basic cellular processes to disease progression.


Network Science

Objective: You will learn how to organize, transform, analyse and visualize data, with a focus on the relational data model, and a detour to semistructured data. You will learn the fundamentals of data science using R environment.