Objective: presenting statistical methods and computational analysis techniques in genomics.
Objective: introducing to the use of computational techniques to solve problems in fluid mechanics.
Objective: introducing the main algorithmic methods for the storage, compression and analysis of large amounts of biological data, with particular emphasis on the treatment of sequencing data produced with next generation sequencing technologies.
Objective: providing fundamental tools and numerical algorithms for solving problems of classical physics and simple problems of quantum physics.
Objective: introducing to the main techniques for the design of algorithms and data structures to manipulate strings, trees and large graphs, in particular to compression techniques and randomization.
Objective: introducing the computational techniques used in molecular modeling and simulation, and illustrating how these techniques can be employed to describe and/ or predict chemical, physical and biological phenomena.
Objective: acquiring genomic knowledge and skills to use bioinformatics method in a rational and efficient way and to interpret the results correctly.
Objective: providing an introduction to numerical methods and techniques for the numerical solution of quantum mechanical problems, especially in atomic physics and condensed matter, with a practical approach.
Objective: providing expertise on optimization techniques, with applications to industrial design.
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: 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.
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: introducing techniques of analysis and statistical Bayesian inference.
Objective: providing methods and results of the elementary statistics mechanics in equilibrium.
Objective: presenting statistical analysis techniques for social networks and other social and economic networks.
Objective: providing an overview, in the context of modern astronomy, to the various cosmic objects and give the basic principles necessary for the determination of their fundamental physical quantities.
Objective: introducing cyber-physical systems, with particular regard to modeling them with hybrid formalisms and the formal verification of their properties.
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: introducing the physical and mathematical principles of fluid dynamics.
Objective: introducing to the foundations of modern cosmological theories.
Objective: introducing advanced computational and statistical techniques for the analysis of clinical data.
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 the elements of the physics of the stellar interior and of important radiative processes in astrophysics.
Objective: presenting the most important conceptual aspects of information retrieval systems, with particular attention to search engines on the Web, discussing basic arguments, current lines of research, and future trends.
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 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: 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 expertise on the motion of fluids inside the human body, especially in the cardiovascular system, with focus on the evaluation in a clinical settings.
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.
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: providing the basic knowledge of fluid dynamics at the environmental scale and of tools for numerical modeling.
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 tools for the analysis of images and signals in the biomedical field.
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 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: introducing machine learning techniques for artificial vision and pattern recognition in sequential data.
Objective: providing a basic knowledge of the physical oceanography and how to integrate theoretical knowledge with experimental measurements.