Courses

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.

Objective: providing advanced knowledge of both theoretical and practical programming in C / C ++ and Python, with particular regard to the principles of object oriented programming and best practices of software development (advanced use of version control systems, continuous integration, unit testing), and introducing the modern technology of algorithms development.

Objective: acquiring genomic knowledge and skills to use bioinformatics method in a rational and efficient way and to interpret the results correctly.

Objective: introducing techniques of analysis and statistical Bayesian inference.

The course aims at introducing the student to modeling and analysis techniques, also data-based, in the area of computational neusciences.

Objective: introducing to the dynamics of highly non-linear processes (turbulence) in fluid dynamics, and to the computational techniques used  to solve such models.

Objective: providing knowledge of the most important theoretical formalism used in quantum chemistry, and of the main computational methods, numerical algorithms, and software tools in the field of quantum chemistry.

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.

Objective: introduce the student to the principles of learning from data based on statistics, and to the scientific treatment of data to obtain new and reproducible knowledge. Some of the main supervised and unsupervised statistical learning techniques are presented.

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.

The course will introduce students to main ideas, problems, data sources and models in georesource identification and management and in energy production.

Objective: providing a thorough and updated knowledge of cosmology issues related to the study of the formation of galaxies, clusters of galaxies and the structure of large-scale Universe in current cosmological models, through analytical, numerical, and statistical techniques for the evolution of disturbances in linear and nonlinear regime.

Objective: presenting statistical methods and computational analysis techniques in genomics.

Objective: introducing advanced computational and statistical techniques for the analysis of clinical data.

Objective: introducing to the foundations of modern cosmological theories. 

The course introduces the main concepts and effects of quantum correlation on  information theory, computation and machine learning.

Objective: providing expertise for the management of clinical and biomedical data from computerized medical records, through the methods of health information technology and of process modeling.

Objective: introduce modern computational techniques and machine learning techniques for the analysis of natural language.

Objective: providing numerical analysis tools for scientific computing, with particular attention to linear algebra, polynomial approximation, numerical integration, numerical solution of ordinary differential equations and partial differential equations, approximation of eigenvalues and eigenvectors.

Objective: providing a basic knowledge of the physical oceanography and how to integrate theoretical knowledge with experimental measurements.

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.

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.

Objective: provide knowledge of the fundamental properties of the dynamics and thermodynamics of the atmosphere, and the formulation and implementation of some simple analytical models of atmospheric dynamical systems.

Objective: providing the ability to understand the functioning and the internal structure of a molecular dynamics program. Being able to write code for molecular dynamics simulations and to analyze the output.

Objective: introducing concepts and techniques for collaborative development of large and complex software systems for industrial applications, including Java, software development lifecycle, best practices in software development as code testing, versioning, and design patterns.

Objective: providing methods and results of the elementary statistics mechanics in equilibrium.