Compulsory Scientific Skills modules

The PhD Programme in Industrial and Information Engineering at the University of Udine offers four intensive “Scientific Skills” core modules, covering topics relevant to electrical, mechanical, information technology, management, and electronic engineering. Modules are taught by academics with expertise across these disciplines. Each module lasts three days and consists of multiple sessions, including demonstration sessions, computer sessions, laboratory sessions, and case-study sessions.The objective is to provide a broad range of topics approached from different perspectives, thereby widening the scientific background of participating PhD fellows and promoting so-called “lateral thinking”.

All PhD students are required to attend at least three of these “Scientific Skills” modules. Typically, Modules 1 and 3 are attended during the first year, while Modules 2 and 4 are attended during the second year. Nevertheless, the modules may also be attended during the third year.

Title Lecturer Duration [h] ECTS Date Time
NUMERICAL METHODS FOR ENGINEERING RESEARCH 16 4
Numerical methods: a critical overview F. Blanchini 4 08/04/2026 9:00-13:00
Design of experiments and data analysis M. Sortino 4 09/04/2026 9:00-13:00
The Monte Carlo method A. Pilotto 2 09/04/2026 14:00-16:00
Generation of pseudo-random numbers R. Rinaldo 2 10/04/2026 9:00-11:00
Ab-initio modelling of technologically relevant materials for electrical and electronic engineering. Part 1 D. Lizzit 2 10/04/2026 14:00-16:00
Ab-initio modelling of technologically relevant materials for electrical and electronic engineering. Part 2 F. Driussi 2 10/04/2026 16:00-18:00
NUMERICAL STRATEGIES FOR ENGINEERING RESEARCH 16 4
Image/video acquisition and processing. N. Martinel 4 08/04/2026 14:00-18:00
Manage the intangible assets of the near future: data and energy. P. L. Montessoro 4 09/04/2026 9:00-13:00
Data and information fusion. L. Snidaro 4 09/04/2026 14:00-18:00
Python data analysis and machine learning libraries. L. Di Gaspero 4 10/04/2026 9:00-13:00
LABORATORIES FOR ENGINEERING RESEARCH” 16 4
Robotic technologies for industrial engineering L. Scalera 4 13/04/2026 9:00-13:00
Electronic equipment for laboratory measurements in power electronics S. Saggini 2 13/04/2026 14:00-16:00
Design for X methods B. Motyl 2 14/04/2026 9:00-11:00
Experimental identification of dynamic systems and manufacturing processes G. Totis 4 14/04/2026 14:00-18:00
Additive Manufacturing, metrology, and reverse engineering E. Vaglio 4 15/04/2026 9:00-13:00
MANAGEMENT ENGINEERING: THEORIES AND CASE STUDIES 16 4
Business Planning M. Podrecca 4 14/04/2026 9:00-13:00
Interdisciplinary research in (management) engineering G. Culot 4 14/04/2026 14:00-18:00
Supply Chain Management M. Marino 4 15/04/2026 9:00-13:00
Researching and Publishing in Operations Management M. Dieste 4 15/04/2026 14:00-18:00

Elective Technical Skills modules

Since 2021, the PhD programme has also offered more specialised “Technical Skills” courses, aimed at providing technical knowledge that students can use to develop their PhD projects.

Students must choose from this pool of courses to meet the 20 ECTS (CFU) requirement, unless they have already completed all four Scientific Skills modules.

Although students are required to obtain 20 ECTS credits (CFU), they  can — are strongly encouraged to  — attend additional “Technical Skills” courses beyond this minimum requirement, where they consider them relevant to their interests and research.

Title Lecturer Duration [h] ECTS Date Time Description
MICROMAGNETIC MODELLING AND SPINTRONICS APPLICATIONS Mario Carpentieri 8 2 29-30/01/2026 This course provides an introduction to micromagnetics and spintronics from an interdisciplinary perspective focused on electrical engineering and physics.
DISCRETE-TIME SIGNAL FILTERING AND INVERSE PROBLEMS IN MEASUREMENT SYSTEMS: THEORY AND APPLICATIONS Giovanni Totis 8 2 09-13/03/2026 The course provides a structured introduction to discrete-time signal filtering and inverse problems as they arise in experimental measurement systems. Starting from the classical representation of linear time-invariant systems, the concept of transmissibility (or frequency response) of an instrument is introduced and experimentally identified. Participants will learn how to measure the transmissibility of a real sensor through experimental testing, how to formulate the corresponding inverse problem, and how to design digital filters to reconstruct the true input signal from distorted or dynamically filtered measurements.
Both parametric and non-parametric approaches will be discussed. The course will cover classical digital filtering concepts (convolution, frequency-domain inversion, regularization, noise amplification issues), as well as model-based techniques for inverse problems, including state-space formulations and Kalman-based filters. Particular emphasis will be placed on the physical meaning of filtering and inversion, on stability and robustness, and on the limits imposed by noise and sensor dynamics.
As a consistent case study, the full workflow will be applied to complex multi-component force measurement systems. Participants will see how the transmissibility of a platform dynamometer can be experimentally identified, inverted to obtain a digital filter, and finally applied to raw discrete-time signals to reconstruct the true three-dimensional input forces.
PYTHON: BASIC PROGRAMMING Luca Di Gaspero 12 3 late april This course introduces Python programming, focusing on its applications in science and engineering. It covers Python’s basic programming constructs, data types, containers, input/output operations, and functional programming principles. the course aims to equip students with the skills to utilize Python effectively in scientific and engineering contexts.
Background: this course is designed for phd students with a fundamental understanding of problem-solving and basic programming concepts. While prior experience in Python is not mandatory, familiarity with any programming language (e.g., c, java, or matlab) will be helpful. Students are expected to know basic concepts such as variables, loops, conditionals, and functions. Although knowledge of numerical-oriented languages like Matlab or R could be beneficial, it is not a prerequisite. The course will build from these foundations and introduce Python-specific tools and libraries for scientific computing.
MACHINE LEARNING WITH PYTHON Luca Di Gaspero 12 3 late april This course is designed for students in science and engineering who wish to harness the power of Python for data analysis and machine learning. It covers essential data analysis techniques, an introduction to machine learning concepts, and the application of Python libraries in these domains.
background: this advanced module builds on the knowledge gained in the first module or assumes a solid understanding of Python programming. Students should already be comfortable with Python’s syntax, and a working knowledge of data structures, functions, and object-oriented programming in Python is essential. Prior exposure to data analysis techniques and libraries like matplotlib and scikit-learn would be beneficial, though critical concepts will be reviewed.
ADVANCED ANALOG DESIGN Stefano Saggini 6 1,5 15-20/06/2026 The course will cover the advanced design of analog systems such as operational amplifiers, current mirrors, sample and hold circuits, and fully differential amplifiers. in the context of amplifiers, circuit configurations of output stages with wide dynamic range and low impedance will be studied, as well as systems with offset cancellation. Finally, the design of mos amplifiers using the gm/id methodology will be introduced.
Background: fundamentals of analog design in CMOS circuits.
RANDOM VIBRATIONS AND SPECTRAL ANALYSIS: THEORY AND EXPERIMENTS Paolo Gardonio / Roberto Rinaldo 12 3 23-24/06/2026 The course is structured in three parts. Part 1 covers the theory of vibration in mechanical systems and structures, focusing on the dynamic response of SISO and MIMO lumped-parameter mechanical systems as well as distributed structures. It also introduces generalized coordinates, natural frequencies, and mode shapes, and develops the concepts of transfer functions and frequency response functions.
Part 2 presents the main elements of signal processing for stochastic vibrations. This includes a review of Fourier theory and random processes, spectral analysis and spectral density estimation, numerical methods for FRF estimation, and discrete-time algorithms implemented in MATLAB/Octave.
Part 3 is a laboratory module dedicated to vibration measurements. Students set up an experimental rig for mechanical FRF testing, perform signal acquisition and digital processing, and learn to estimate power spectral density functions and FRFs from measured data.
Background: fundamentals of dynamics of mechanical systems, basic knowledge of probability theory and fourier analysis, basic knowledge of matlab/octave programming.
LYAPUNOV METHODS IN ROBUSTNESS Franco Blanchini 12 3 TBD Any model of a real system presents inaccuracies. This is the reason why robustness with respect to system variations is perhaps one of the most important aspects in the analysis and control of dynamical systems. In simple words, a system which has to guarantee certain properties, is said robust if satisfies the requirements not only for its nominal values but also in the presence of perturbations. In this course we present an overview of a specific approach to system robustness and precisely that based on the lyapunov theory. although this approach is a classical one, it is still of great interest in view of the powerful tools it considers. We first introduce some generic concepts related to the theory of lyapunov functions and control lyapunov functions. Then we investigate more specific topics such that the stability and stabilization via quadratic lyapunov functions. We subsequently discuss some classes of non–quadratic functions such as the polyhedral ones. We finally briefly present some successful applications of the theory.
Background: system theory and control theory.