The PhD Course in Industrial and Information Engineering at the University of Udine offers four intensive technical courses. Each Module lasts three days and is organised in six sessions of approximately three hours each. The sessions are organised as demonstration-sessions, computer-sessions, lab-sessions and case-study-sessions.

Each module has a multidisciplinary layout that covers topics of interest for electric, mechanical, information technology, management and electronic engineering. Also, each module is taught by academics with backgrounds in these disciplines. The objective is to offer a broad spectrum of topics taught from different perspectives, which should widen the scientific background of the participating fellows and promote the so called “lateral thinking”.

All PhD fellows shall attend the courses. First year PhD Fellows shall attend Module 1 and 3 while second year PhD Fellows shall attend Module 2 and 4. Third year PhD fellows shall attend the courses that they had not attended in previous years. PhD Fellows have to gain no less than 20 CFUs in three years. The present Training Courses do provide CFUs. Please use the webpage on the PhD Notebook to register the CFUs associated to your training and teaching activities, and ask your tutor for the number of CFUs to be associated to each activity.

While these Scientific Skills Training Courses are tutorial lectures intended to provide a broad idea of the research topics developed in our Department and in particular by the members of the PhD board, starting from year 2021 our PhD programme also offers more specific courses, whose goal is to convey a more technical knowledge that students may employ for the development of their PhD project

PhD students can attend further optional more specialised training courses.

Here is the list of courses planned in 2025:

Title: “Multiaxial fatigue assessment of metallic structural components“,

Lecturer: Prof. Luca Susmel

Times and location: 6 hrs 30 April 2025, 9.30-12.30 and 14.00-17.00,, Sala GIALLA

Dates: 30th April 2025

Module description: The course delves into the complexities of multiaxial fatigue assessment, examining the underlying mechanics and failure mechanisms in great detail. 

Title: “Micromagnetic Modelling and Spintronics applications”,

Lecturer: Prof. Mario Carpentieri

Duration: TBD

Dates: 14/07/2025 and 18/07/2025

Module description: This course provides an introduction to micromagnetics and Spintronics from an interdisciplinary perspective focused on electrical engineering and physics.

Title: “Advanced analog design”,

Lecturer: Stefano Saggini

Duration: 6 hrs (CFU: 1.5)

Dates: 14/07/2025 and 18/07/2025

Module description: 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.

Title: “Python: basic programming ”

Lecturer: Prof. Luca Di Gaspero

12 hours, 3 ECTS

Dates: 

11/03/2025 (4h), 18/03/2025 (4h), 20/03, 9:00–13:00 (4h)

Time: 9:00 am – 11:00 am

Where: Sala Bianca (L1-15-CD)

Module description: This module 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.

Title: “Machine Learning with Python”

Lecturer: Prof. Luca Di Gaspero

12 hours, 3 ECTS

Dates: 25/03/2025 (4h), 01/04/2025 (4h), 10/04/2025 (4h)

Time: 9:00 am – 11:00 am

Where: Sala Bianca (L1-15-CD)

Module description: This module 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.

Title: “Random vibrations and spectral analysis: theory and experiments,

Lecturers: Paolo Gardonio e Roberto Rinaldo

Duration: 12, CFU: 3,

When: 24-25/09/2025

Course Description
• Part 1: theory of vibration of mechanical systems and structures
– Dynamic response of SISO and MIMO lumped parameter mechanical systems
– Dynamic response of distributed structures
– Generalised coordinates: natural frequencies and natural modes
– Transfer Function and Frequency Response Function
• Part 2: elements of signal processing for stochastic vibrations
– Summary of Fourier theory and random processes
– Spectral analysis and Spectral Density Estimation
– FRF estimation numerical methods
– Discrete time algorithms with MatLab/Octave
• Part 3: laboratory for vibration measurements
– Setup of a rig for mechanical FRF measurements
– Signal acquisition and digital processing
– Estimate of Power Spectral Density functions
– Estimate of FRF from measured data
Background: Fundamentals of dynamics of mechanical systems, basic knowledge of probability theory and Fourier analysis, basic knowledge of MatLab/Octave programming

Title: “Metal Plasticity and Large Deformations: Theory and Numerical Approaches”,

Lecturers: Manish Kumar

Duration: 10 hrs (CFU: 2.5)

When: 03/03/2025 – 07/03/2025

Time: 10:30 am – 12:30 am

Where: Sala Gialla

Course description:
• Theory of post elastic limit analysis for metals: introduction of the yielding criterion, hardening behaviours, hardening laws (linear, multi-linear and non-linear), and calculation for the plastic behaviour.
• Introduction of large deformations in the finite element simulations with the inclusion of metal plasticity.
• Hands-on MATLAB code and commercial software to perform finite element simulations for large deformation and plasticity.
Background: Basic knowledge of finite element theory and MATLAB programming.

Title: “Introductory Quantum Mechanics for Engineers”,

Lecturer Giancarlo Panizzo

Duration: 12 hrs (CFU: 3)

Dates:

Monday 3/02 and 10/02 time: 11:00-13:00
Tuesday 4/02 and 11/02 time: 11:00-13:00
Thursday 6/02 and 13/02 time: 11:00-13:00

Course description: Quantum mechanics drove a technological revolution in the past century, its applications being now widespread in real life devices. This lecture introduces to the quantum principles which govern physics behaviors at nanoscales, specifically targeting master degree Engineering students. How does a quantum particle behave in a pipe? What is the quantum analogue of a classical spring? How does this relate to energy being quantized? The answers to these questions will help deepening the understanding of theprinciples which hide under the operation of diodes, transistors, LEDs, photovoltaic cells and modern physics detectors.
Background: Basic physics and python programming knowledge.

Title: “Lyapunov methods in robustness”,

Lecturer:Franco Blanchini

Duration: 12 hrs (CFU: 3)

When: 03/03/2025 e 10/03/2025

Course description: 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,

Title: “Beyond the Drift-Diffusion model: Boltzmann Transport Equation (BTE) for Electron Devices Modelling”,

Lecturer: David Esseni

Duration: 12 hrs (CFU: 3)

When: from 02/09/2025 to 05/09/2025

Course descripton: This course opens by recalling the physical picture behind the widely used Drift Diffusion (DD) model for electronic transport in electron devices, and then introduces the Boltzmann Transport Equation (BTE). The larger and deeper information conveyed by the BTE compared to the DD framework is discussed and several applications of the BTE are introduced. Then the Fermi’s rule for the calculation of scattering rates is explained and applied to a few relevant scattering mechanisms. The course will also address mobility calculations, provide hints to numerical methods to solve the BTE, as well as to the derivation of the Drift Diffusion model from the BTE.
Background: Drift diffusion model and basic knowledge of the physics of electron devices.