Training and Research

PhD Programme Courses/classes - 2024/2025

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!

Instructions for teachers: lesson management

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

Credits

5

Language

English

Class attendance

Free Choice

Location

VERONA

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

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

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

Course lessons
PhD Schools lessons

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Guidelines for PhD students

Below you will find the files that contain the Guidelines for PhD students and rules for the acquisition of ECTS credits (in Italian: "CFU") for the Academic Year 2023/2024.

Documents

Title Info File
File octet-stream Annual credit sheet octet-stream, it, 20 KB, 05/04/24
File pdf Basic expected outcomes pdf, en, 131 KB, 05/04/24
File pdf Competenze attese pdf, it, 129 KB, 05/04/24
File pdf Prodotti minimi attesi pdf, it, 126 KB, 05/04/24
File pdf Specific learning outcomes pdf, en, 133 KB, 05/04/24