Training and Research

PhD Programme Courses/classes - 2023/2024

Optical Imaging technique: principles and applications

Credits: 1

Language: English

Teacher:  Federico Boschi

Nanomaterials and green chemistry: from synthesis to applications

Credits: 1

Language: English

Teacher:  Adolfo Speghini

Tecniche di deposizione a film sottile per biomateriali, applicazioni biomediche e energia solare

Credits: 1,5

Language: English

Teacher:  Alessandro Romeo

Protein crystallization and structure solving: classical and novel methods

Credits: 1,5

Language: English

Teacher:  Massimiliano Perduca

Forensic toxicology and analitics

Credits: 6

Language: English

Teacher:  Rossella Gottardo

Erasmus + seminars

Credits: 5,3

Language: English

Forensic Genetics

Credits: 4

Language: English

Teacher:  Stefania Turrina

An introduction to NMR spectroscopy for the study of biomacromolecules

Credits: 1

Language: English

Teacher:  Mariapina D'Onofrio

Tecniche di immagine per l'analisi della composizione corporea

Credits: 1

Language: English

Teacher:  Carlo Zancanaro

Application of multimodal imaging techniques in the study of the skeletal muscle

Credits: 0,5

Language: English

Teacher:  Barbara Cisterna

Biophysical Methods for the Analysis of Protein-Ligand interactions

Credits: 1

Language: English

Teacher:  Filippo Favretto

Engineering photosynthesis to enhance productivity

Credits: 1

Language: English

Teacher:  Stefano Cazzaniga

Inibitori e stabilizzatori delle interazioni proteina-proteina

Credits: 0,5

Language: English

Teacher:  Francesca Munari

Luminescent lanthanide complexes for bioimaging and sensing applications

Credits: 0,5

Language: English

Teacher:  Fabio Piccinelli

Magnetic Resonance Imaging for the characterization of the central and peripheral nervous system

Credits: 1,5

Language: English

Teacher:  Pietro Bontempi

Markers of chronic alcohol abuse: analytical and interpretative aspects

Credits: 1

Language: English

Teacher:  Federica Bortolotti

Surface Metrology with optical techniques

Credits: 1

Language: English

Teacher:  Claudia Daffara

Synthesis, characterization and applications of luminescent nanostructured materials (titolo ridotto rispetto AA 2022-23)

Credits: 1

Language: English

Teacher:  Francesco Enrichi

Tecniche ultrastrutturali e citochimiche per individuare le nanoparticelle in cellule e tessuti

Credits: 1

Language: English

Teacher:  Manuela Malatesta

virtopsy: general aspects and new trends

Credits: 0,5

Language: English

Teacher:  Francesco Ausania

PhD school courses/classes - 2023/2024

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

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.

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, no confirmation e-mail will be sent after signing up.

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

2

Language

English

Class attendance

Free Choice

Location

VERONA

Learning objectives

An introduction to the fundamentals of generalized regression models will be given in this course, with a focus on models for count, binary, and categorical data. These types of response variables are widely used in industrial applications as well as observational and experimental research.
Upon successful completion of the course, students will be able to:
• Describe the general structure of a GLM and similarities and differences with linear models
• Estimate and interpret a logistic regression model
• Estimate and interpret a Poisson regression model
• Know of issues and some strategies for dealing with overdispersion in some generalised linear models (GLMs)

Prerequisites and basic notions

This course assumes a good understanding of probability and mid-level knowledge of linear regression theory.

Program

The course covers methods for regression analysis of responses that do not follow the normal distribution, especially of discrete responses. We will learn to understand some of the common statistical methods for fitting regression models to such data. In particular, we will consider logistic regression, Poisson regression and log-linear models. The lecture focuses on the development, theoretical justification, and interpretation of these methods.

When and where

Teaching forms mainly consist of lectures (8h) and exercises proposed by the teacher. The teaching material (slides of the theoretical lessons) is made available to the students on the e-learning web page of the course (Moodle platform). Lessons will be delivered via Zoom. Full attendance is required.

Learning assessment procedures

There is no exam

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Assessment

There is no exam, hence there is also no definition of the evaluation criteria.

Criteria for the composition of the final grade

There is no grade because there is no exam.

Scheduled Lessons

When Classroom Teacher topics
Tuesday 19 March 2024
14:30 - 16:30
Duration: 2:00 AM
To be defined Lucia Cazzoletti Introduction to generalised linear models. Review of the general linear model: assumptions of the linear model (independence of observations, homoskedasticity of errors, linearity of coeffiicents) , least squares estimation and maximum likelihood estimation. Main features of the genralised linear model: i) the probability distribution function of the random component of the response variable belongs to the exponential family, ii) a differentiable and monotonic link function relates the mean of the response variable to the linear predictor, a linear combination of coefficients and explanatory variables.
Tuesday 26 March 2024
14:30 - 16:30
Duration: 2:00 AM
To be defined Lucia Cazzoletti Introduction to the theoretical basis of logistic regression model, commonly used for binary (proportion/percentage) data, as a generalised linear model. Binomial distribution for the outcome binary variable. The link between probability and logodds. Maximum likelihood estimation of the coefficients of the model. Interpretation of the meaning of the regression coefficients and their statistical significance.
Wednesday 03 April 2024
14:30 - 16:30
Duration: 2:00 AM
To be defined Lucia Cazzoletti Introduction to the theoretical basis of Poisson regression model, commonly used for count data, as a generalised linear model. Poisson distribution for the outcome variable. Maximum likelihood estimation of the coefficients of the model. Interpretation of the meaning of the regression coefficients and their statistical significance. Use of the offset to take into account the different exposure of subjects. Extensions of the Poisson Regression Model: Negative binomial regression model (NBRM), Zero-inflated poisson (ZIP) model, Zero-truncated count data model.
Wednesday 24 April 2024
14:30 - 16:30
Duration: 2:00 AM
Aula virtuale - Lezione online Lucia Cazzoletti Using Deviances to Compare Models for Logistic and for Poisson Regression Models. Use of the Likelihood Ratio Test to assess the presence of overdispersion. Some hints about the log-linear model in the presence of contingency tables

Faculty

A B C D E F G M N P R S T Z

Ausania Francesco

symbol email francesco.ausania@univr.it symbol phone-number 0458127455

Bontempi Pietro

symbol email pietro.bontempi@univr.it symbol phone-number +39 045 802 7614

Bortolotti Federica

symbol email federica.bortolotti@univr.it symbol phone-number 045 8124618

Boschi Federico

symbol email federico.boschi@univr.it symbol phone-number +39 045 802 7272

Calderan Laura

symbol email laura.calderan@univr.it symbol phone-number 0458027562

Carta Angela

symbol email angela.carta@univr.it symbol phone-number +39 045 812 8270

Cazzaniga Stefano

symbol email stefano.cazzaniga@univr.it symbol phone-number +39 045 8027075

Cisterna Barbara

symbol email barbara.cisterna@univr.it symbol phone-number +39 045 802 7564

Daffara Claudia

symbol email claudia.daffara@univr.it symbol phone-number +39 045 802 7942

D'Onofrio Mariapina

symbol email mariapina.donofrio@univr.it symbol phone-number 045 802 7801

Enrichi Francesco

symbol email francesco.enrichi@univr.it symbol phone-number +390458027051

Favretto Filippo

symbol email filippo.favretto@univr.it symbol phone-number 045 802 7865

Gottardo Rossella

symbol email rossella.gottardo@univr.it symbol phone-number 045 8124247

Malatesta Manuela

symbol email manuela.malatesta@univr.it symbol phone-number +39 045 802 7569

Manfredi Riccardo

symbol email riccardo.manfredi@univr.it symbol phone-number +39 045 802 74 89

Munari Francesca

symbol email francesca.munari@univr.it symbol phone-number +39 045 802 7920

Nardon Chiara

symbol email chiara.nardon@univr.it

Perduca Massimiliano

symbol email massimiliano.perduca@univr.it symbol phone-number +39 045 8027984

Piacentini Giorgio

symbol email giorgio.piacentini@univr.it symbol phone-number +39 045 812 7120

Piccinelli Fabio

symbol email fabio.piccinelli@univr.it symbol phone-number +39 045 802 7097

Porru Stefano

symbol email stefano.porru@univr.it symbol phone-number Diretto 045 812 4294 - Segreteria 045 802 7421-7422

Romeo Alessandro

symbol email alessandro.romeo@univr.it symbol phone-number +39 045 802 7936; Lab: +39 045 802 7808

Sbarbati Andrea

symbol email andrea.sbarbati@univr.it symbol phone-number +39 045 802 7266

Speghini Adolfo

symbol email adolfo.speghini@univr.it symbol phone-number +39 045 8027900

Turrina Stefania

symbol email stefania.turrina@univr.it symbol phone-number 045/8027622

Zancanaro Carlo

symbol email carlo.zancanaro@univr.it symbol phone-number 045 802 7157 (Medicina) - 8425115 (Scienze Motorie)

PhD students

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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 pdf Dottorandi: linee guida generali (2023/2024) pdf, it, 111 KB, 26/02/24
File pdf PhD students: general guidelines (2023/2024) pdf, en, 127 KB, 26/02/24