Training and Research

PhD school courses/classes - 2024/2025

Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).

1. PhD students must obtain a specified number of CFUs each year by attending teaching activities offered by the PhD School.
First and second year students must obtain 8 CFUs. Teaching activities ex DM 226/2021 provide 5 CFUs; free choice activities provide 3 CFUs.
Third year students must obtain 4 CFUs. Teaching activities ex DM 226/2021 provide 2 CFUs; free choice activities provide 2 CFUs.
More information regarding CFUs is found in the Handbook for PhD Students: https://www.univr.it/phd-vademecum

2. Registering for the courses is not required unless explicitly indicated; please consult the course information to verify whether registration is required or not. When registration is actually required, instructions will be sent well in advance. No confirmation e-mail will be sent after signing up. Please do not enquiry: if you entered the requested information, then registration was silently successful.

3. When Zoom links are not explicitly indicated, courses are delivered in presence only.

4. All information we have is published here. Please do not enquiry for missing information or Zoom links: if the information you need is not there, then it means that we don't have it yet. As soon as we get new information, we will promptly publish it on this page.

Summary of training activities

Teaching Activities ex DM 226/2021: Linguistic Activities

Teaching Activities ex DM 226/2021: Research management and Enhancement

Teaching Activities ex DM 226/2021: Statistics and Computer Sciences

Teaching Activities: Free choice

THE EMPIRICAL PHENOMENOLOGICAL METHOD (EPM): THEORETICAL FOUNDATION AND EMPIRICAL APPLICATION IN EDUCATIONAL AND HEALTHCARE FIELDS

Credits: 2

Language: English

Teacher:  Luigina Mortari

DOTTORATO E MERCATO DEL LAVORO: WORKSHOP FORMATIVI PER DOTTORANDI E NEO-DOTTORI DI RICERCA

Credits: 4

Language: Italian
ARE YOU SURE YOU CAN DEFEAT A CHATBOT?

Credits: 1

Language: Italian
MEETING UKRAINE: THE IMPACT OF WAR AND FUTURE OPPORTUNITIES

Credits: 1

Language: Italian
OPEN SCIENCE: THE MIGHTY STICK AGAINST "BAD" SCIENCE

Credits: 2

Language: English

Teacher:  Michele Scandola

Differential diagnosis of demyelinating diseases of the central nervous system

Credits: 2

Language: Italiano; English

Teacher:  Alberto Gajofatto

EMOTIONS, BELIEFS, AND SKILLS TO FACE CLIMATE CHANGE AND EMBRACE CLIMATE ACTION

Credits: 0.5

Language: English

Teacher:  Isolde Martina Busch

COMPUTATIONAL MECHANISMS UNDERLYING SENSORIMOTOR LEARNING

Credits: 4.5

Language: English

Teacher:  Matteo Bertucco

CSF DYNAMICS: ANATOMICAL AND FUNCTIONAL FEATURES

Credits: 0.5

Language: English

Teacher:  Alberto Feletti

sleep related disoders: focus on REM and NREM parasomnia and SR movement disorders

Credits: 1.5

Language: italiano o inglese

Teacher:  Elena Antelmi

Tecniche di immagine per l'analisi della composizione corporea

Credits: 1

Language: Inglese/English

Teacher:  Carlo Zancanaro

Tecniche di ricerca in neuroscienze: misurare e modulare l'attività neuronale

Credits: 2.3

Language: non prevista

Teacher:  Giuseppe Busetto

METHODS OF EXPERIMENTAL DATA ANALYSIS AND REPORT WITH MATLAB, A GENTLE INTRODUCTION  (2024/2025)

Teacher

Giuseppe Bertini

Referent

Giuseppe Bertini

Credits

5

Language

English

Class attendance

Free Choice

Location

VERONA

Seminars 0

Learning objectives

Matlab is a numerical computing environment and high-level programming language. In the context of biomedical research, Matlab can be used to automate a wide variety of tasks, including record keeping, data analysis, and report generation.

Matlab favors scripting, i.e., writing in a text file a sequence of commands that can be executed reliably and consistently. Used properly, this method can be orders of magnitude more efficient and error-proof than interactive sessions with the most common computing platforms (e.g., Excel, SPSS, etc.) A well-tested script can be seamlessly run against any number of datasets and applied to similar experimental situations. Importantly, the scripts themselves represent accurate documentation of the performed analyses.

While a relatively steep learning curve is inevitable, especially in the absence of any computer programming background, Matlab can be mastered in small, incremental steps.

This mini course is intended as a general introduction to the concept of automated data analysis and report. The Matlab interface and the fundamentals of the scripting language will be presented first. A small set of common data analysis problems will then be tackled in the Matlab environment.

Please note that the available time is not sufficient to provide a detailed, in-depth knowledge of the language. My goal is to demystify the technology and provide insight into what can be accomplished and what the main hurdles are. Further training will be required to become fully proficient.

Bibliography

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Didactic methods

Si richiede la frequenza di almeno 16 delle 20 ore previste.