Training and Research
PhD Programme Courses/classes
This page shows the PhD course's training activities for the academic year 2024/2025. Further activities will be added during the year. Please check regularly for updates!
Modelli dinamici e simulazione di sistemi multibody
Credits: 5
Language: English
Teacher: Iacopo Tamellin
Advanced techniques for acquisition of biomedical images
Credits: 1
Language: Ingelese
Teacher: Pietro Bontempi, Federico Boschi
Theranostics: from materials to devices
Credits: 1
Language: english
Teacher: Nicola Daldosso, Tommaso Del Rosso
Nanomaterials: synthesis, characterization and applications
Credits: 1
Language: English
Teacher: Francesco Enrichi, Tommaso Del Rosso
Brain Computer Interfaces
Credits: 3
Language: Inglese
Teacher: Silvia Francesca Storti
Algorithmic motion planning in robotics
Credits: 1
Language: Italian
Teacher: Paolo Fiorini
Data visualization
Credits: 1
Language: Inglese
Teacher: Andrea Giachetti
Modellazione e analisi 3D
Credits: 1
Language: Inglese
Teacher: Andrea Giachetti
Modelli di Intelligenza Artificiale Spiegabile: stato dell'arte, promesse e sfide
Credits: 2.5
Language: Inglese
Teacher: Gloria Menegaz
Foundation of Robotics Autonomy
Credits: 1
Language: Italian
Teacher: Paolo Fiorini
Generative AI
Credits: 1.5
Language: English
Teacher: Francesco Setti
Modeling and Verification of Digital Systems
Credits: 1.5
Language: Italian
Teacher: Franco Fummi, Nicola Bombieri, Graziano Pravadelli
Soft robotics: from nature to engineering
Credits: 1.5
Language: English
Teacher: Francesco Visentin
Techniques and algorithms for biomechanics of movement
Credits: 2.5
Language: English
Teacher: Roberto Di Marco
Techniques and algorithms for biomechanics of movement (2024/2025)
Teacher
Referent
Credits
2.5
Language
English
Class attendance
Free Choice
Location
VERONA
Learning objectives
The course aims to provide PhD students with basic knowledge, taxonomy, principles and modelling of human motion, presenting state-of-the-art instruments (such as sterophotogrammetry, markerless systems and wearable devices) and approaches to evaluate the mechanics of motion for clinical evaluation and monitoring, and sports purposes. The course will provide attendees with the knowledge and language to interact with partners with different basic training (e.g., clinicians, physiotherapists, sports scientists) to effectively act on broad projects.
Prerequisites and basic notions
Solid background in geometry and algebra, and signal analysis. Knowledge of MATLAB and Python programming languages.
Bibliography
Didactic methods
Lectures: theory and demonstration of data analysis examples from various contexts.
Learning assessment procedures
Presentation and discussion of a project work.
Assessment
The project work must be presented according to the criteria valid for high-level scientific papers. The candidate must demonstrate to have correctly framed the problem within the relevant literature, understood the significance of the problem, use methodological rigor, and be able to effectively present the results while discussing their possible implications.
Criteria for the composition of the final grade
Project work evaluation according to the given criteria.
Sustainable Development Goals - SDGs
This initiative contributes to the achievement of the Sustainable Development Goals of the UN Agenda 2030. More information on sustainability
