Training and Research
PhD Programme Courses/classes - 2023/2024
How to measure photosynthetic traits in plants
Credits: 1
Language: English
Teacher: Matteo Ballottari
Perspectives for crop protection under the European green deal
Credits: 1
Language: English
Teacher: Annalisa Polverari
Food science
Credits: 1
Language: Italian
Teacher: Gianni Zoccatelli
Consideration of insects as a source of dietary protein for human consumption
Credits: 0,5
Language: English
Teacher: Nicola Mori
Introduction to Mass Spectrometry and Proteomics
Credits: 1
Language: English
Teacher: Jessica Brandi
Peptidi segnali coinvolti nel sviluppo della piante e nella risposta allo stress
Credits: 1
Language: English
Teacher: Tiziana Pandolfini
Metodologie della ricerca in ambito biomedico
Credits: 1
Language: English
Teacher: Alberto Vezzelli & Elena Vicentini, Daniela Cecconi, Chiara Piubelli
Protein Engineering for Enzyme Optimisation
Credits: 1
Language: English
Teacher: Salvatore Fusco
Synchrotron radiation: basics and applications in environmental samples
Credits: 1
Language: English
Teacher: Prof.ssa Beatrice Giannetta
Fluorescence and confocal microscopy for plant biologists
Credits: 1
Language: English
Teacher: Mauro Commisso
Plant Sciences
Credits: 1
Language: English
Teacher: Sara Zenoni, Anita Zamboni
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).
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, 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: as soon as we get new information, we will promptly publish it on this page.
Teaching Activities ex DM 226/2021: Linguistic Activities
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Law and Economics]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
Teacher: Monica Antonello
ENGLISH FOR ACADEMIC WRITING SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Law and Economics]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
Teacher: Monica Antonello
ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
Teaching Activities ex DM 226/2021: Research management and Enhancement
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Arts and Humanities]
Credits: 2,5
Language: Italian
Teacher: Donatella Boni
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Law and Economics]
Credits: 2,5
Language: Italian
Teacher: Luisella Zocca
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Scientific Area]
Credits: 2,5
Language: Italian
Teacher: Elena Scanferla
Teaching Activities ex DM 226/2021: Statistics and Computer Sciences
INTRODUCTION TO PROBABILITY (MODULE I)
Credits: 1
Language: English
Teacher: Marco Minozzo
INTRODUCTION TO PROBABILITY (MODULE II)
Credits: 1
Language: English
Teacher: Marco Minozzo
INTRODUCTION TO STATISTICAL INFERENCE
Credits: 1
Language: English
Validità e affidabilità delle misure e dei test diagnostici
Credits: 0,5
Language: English
Teacher: Alessandro Marcon
BASIC LEVEL STATISTICS
Credits: 2,5
Language: English
BASIC LEVEL STATISTICS
Credits: 2,5
Language: Italian
Statistical analysis with R - module I
Credits: 1
Language: Italian
Teacher: Erica Secchettin
GENERALIZED LINEAR MODELS: LOGISTIC REGRESSION, LOGLINEAR MODEL, POISSON MODEL
Credits: 2
Language: English
Teacher: Lucia Cazzoletti
Disegno dello studio nella ricerca osservazionale e sperimentale
Credits: 1,5
Language: English
Teacher: Alessandro Marcon
Calcolo della numerosità campionaria in funzione di una precisione o potenza statistica prefissata
Credits: 1
Language: English
Teacher: Giuseppe Verlato
Introduzione alla meta-analisi per la ricerca biomedica (revisione della letteratura, raccolta dei dati, costruzione del database)
Credits: 1
Language: English
Teacher: Giuseppe Verlato
Applicazioni della meta-analisi in campo epidemiologico e medico
Credits: 1
Language: Inglese
Teacher: Giuseppe Verlato
Survival analysis: log-rank test, Kaplan-Meier survival curves, Cox regression model
Credits: 1,5
Language: Inglese - English
Teacher: Simone Accordini
INTERMEDIATE STATISTICS [Recommended for Human Sciences]
Credits: 2,5
Language: English
INTERMEDIATE STATISTICS [Tutti i corsi di studio]
Credits: 2,5
Language: English
Statistical analysis with R - module II
Credits: 2
Language: Italian
Teacher: Erica Secchettin
Teaching Activities: Free choice
PROTECTING PSYCHOLOGICAL WELL-BEING IN THE PHD PROGRAM: WHAT DO WE NEED TO CONSIDER FOR BEING A GOOD SCIENTIST: BEST PRACTICE AND THE ETHICS OF SCIENCE
Credits: 1
Language: inglese
Teacher: Paola Cesari
QUANDO LA RICERCA SI FA ETICA (PERCORSO ORGANIZZATO E FINANZIATO DAL TEACHING AND LEARNING CENTER DI UNIVR)
Credits: 2
Language: Italian
Teacher: Roberta Silva
LA COMUNICAZIONE UMANISTICA: OPPORTUNITA' E RISCHI
Credits: 1
Language: Italiano
Italian Poetry abroad
Credits: 1
Language: Italiano
Teacher: Massimo Natale
BUSINESS MODEL CANVAS PILL
Credits: 1,5
Language: English
IMPARA IL MARKETING DIGITALE
Credits: 1,5
Language: English
APPROCCI E METODOLOGIE PARTECIPATIVE NELLA RICERCA CON GLI ATTORI DEL TERRITORIO
Credits: 1,5
Language: Italian
Teacher: Cristiana Zara
DOING INTERVIEWS IN QUALITATIVE RESEARCH
Credits: 1,5
Language: English
Teacher: Chiara Sita'
DIFFERENTIAL DIAGNOSIS OF DEMYELINATING DISEASES OF THE CENTRAL NERVOUS SYSTEM
Credits: 2
Language: English
Teacher: Alberto Gajofatto
IL SONNO E I SUOI DISTURBI: FOCUS SULLE PARASONNIE E I DISTURBI DEL MOVIMENTO IN SONNO
Credits: 1
Language: English
Teacher: Elena Antelmi
IMAGING TECHNIQUES FOR BODY COMPOSITION ANALYSIS
Credits: 1
Language: English
Teacher: Carlo Zancanaro
OPEN SCIENCE: THE MIGHTY STICK AGAINST "BAD" SCIENCE
Credits: 2
Language: English
Teacher: Alberto Scandola
THE EMPIRICAL PHENOMENOLOGICAL METHOD (EPM): THEORETICAL FOUNDATION AND EMPIRICAL APPLICATION IN EDUCATIONAL AND HEALTHCARE FIELDS
Credits: 2
Language: English
THE PATHWAY OF OXYGEN: CAUSE OF HYPOXEMIA
Credits: 1
Language: English
Teacher: Carlo Capelli
INTRODUCTION TO STATISTICAL INFERENCE (2023/2024)
Learning objectives
The purpose of the module is to explain, at an elementary level, the conceptual basis of the classical (frequentist) approach to statistical inference. The topics will be illustrated and explained through many examples. Students are expected to acquire the language and the concepts needed to better understand the inferential procedures required for their subjects.
Prerequisites and basic notions
There are no particular learning requirements. Students should have already been introduced (though at an elementary level) to probability and statistics. Students should also have some confidence in elementary set theory and mathematical calculus.
Program
- Revision of limit theorems: weak law of large numbers; central limit theorem.
- Random samples, sample statistics and sampling distributions; normal and Bernoulli populations; sample mean, sample variance and sample proportion.
- Point estimation: estimators, unbiasedness, efficiency, mean square error, consistency.
- Interval estimation: pivotal quantity; paradigmatic examples.
- Hypothesis testing: type I and type II errors; critical value; confidence level; power; test statistic; observed significance level, paradigmatic examples.
Bibliography
Didactic methods
Lessons will be delivered via Zoom; recordings will be made available by the lecturer. Attendance is not required, but passing a written test is required to obtain credits.
26 February 2024, 14:00-17:00
27 February 2024, 14:00-17:00
28 February 2024, 14:00-16:00
Moodle link
Learning assessment procedures
The final assessment will be through a written paper. Alternatively, there will be a Moodle QUIZ.
Scheduled Lessons
| When | Classroom | Teacher | topics |
|---|---|---|---|
|
Monday 26 February 2024 14:00 - 17:00 Duration: 3:00 AM |
https://moodledidattica.univr.it/course/view.php?id=15631 | Marco Minozzo | INTRODUCTION TO STATISTICAL INFERENCE |
|
Tuesday 27 February 2024 14:00 - 17:00 Duration: 3:00 AM |
https://moodledidattica.univr.it/course/view.php?id=15631 | Marco Minozzo | INTRODUCTION TO STATISTICAL INFERENCE |
|
Wednesday 28 February 2024 14:00 - 16:00 Duration: 2:00 AM |
https://moodledidattica.univr.it/course/view.php?id=15631 | Marco Minozzo | INTRODUCTION TO STATISTICAL INFERENCE |
PhD students
antonello.amelii@univr.it
michele.avesani@univr.it
giulia.bertazzoli@univr.it
martina.bussola@univr.it
marco.carlomagno@univr.it
carmine.carratore@univr.it
edoardo.ceschi@univr.it
francesco.fontanive@univr.it
sara.gorrieri@univr.it
riccardo.greco@univr.it
filippo.lucchini@univr.it
nicola.mase@univr.it
tea.miotti@univr.it
simone.musollini@univr.it
matteo.paini@univr.it
marco.pezzuto@univr.it
denise.pivotto@univr.it
alessia.raneri@univr.it
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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.
