Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

A.A. 2020/2021

Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
I semestre Oct 1, 2020 Jan 29, 2021
II semestre Mar 1, 2021 Jun 11, 2021
Exam sessions
Session From To
Sessione invernale d'esame Feb 1, 2021 Feb 26, 2021
Sessione estiva d'esame Jun 14, 2021 Jul 30, 2021
Sessione autunnale d'esame Sep 1, 2021 Sep 30, 2021
Degree sessions
Session From To
Sessione estiva di laurea Jul 16, 2021 Jul 16, 2021
Sessione autunnale di laurea Oct 11, 2021 Oct 11, 2021
Sessione autunnale di laurea - Dicembre Dec 6, 2021 Dec 6, 2021
Sessione invernale di laurea Mar 9, 2022 Mar 9, 2022
Holidays
Period From To
Festa dell'Immacolata Dec 8, 2020 Dec 8, 2020
Vacanze Natalizie Dec 24, 2020 Jan 3, 2021
Epifania Jan 6, 2021 Jan 6, 2021
Vacanze Pasquali Apr 2, 2021 Apr 5, 2021
Festa del Santo Patrono May 21, 2021 May 21, 2021
Festa della Repubblica Jun 2, 2021 Jun 2, 2021
Vacanze estive Aug 9, 2021 Aug 15, 2021

Exam calendar

Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff

A B C D F G L M P R S T U V Z

Assfalg Michael

michael.assfalg@univr.it +39 045 802 7949

Avesani Linda

linda.avesani@univr.it +39 045 802 7839

Ballottari Matteo

matteo.ballottari@univr.it 045 802 7098

Bassi Roberto

roberto.bassi@univr.it 045 802 7916; Lab: 045 802 7915

Bellin Diana

diana.bellin@univr.it 045 802 7090

Bettinelli Marco Giovanni

marco.bettinelli@univr.it 045 802 7902

Bolzonella David

david.bolzonella@univr.it 045 802 7965

Buffelli Mario Rosario

mario.buffelli@univr.it +39 0458027268

Cazzaniga Stefano

stefano.cazzaniga@univr.it +39 045 8027807

Cecconi Daniela

daniela.cecconi@univr.it +39 045 802 7056; Lab: +39 045 802 7087

Chignola Roberto

roberto.chignola@univr.it 045 802 7953

Chiurco Carlo

carlo.chiurco@univr.it +390458028159

Crimi Massimo

massimo.crimi@univr.it 045 802 7924; Lab: 045 802 7050

Dall'Osto Luca

luca.dallosto@univr.it +39 045 802 7806

Delledonne Massimo

massimo.delledonne@univr.it 045 802 7962; Lab: 045 802 7058

Dominici Paola

paola.dominici@univr.it 045 802 7966; Lab: 045 802 7956-7086

Fiammengo Roberto

roberto.fiammengo@univr.it 0458027038

Frison Nicola

nicola.frison@univr.it 045 802 7965

Furini Antonella

antonella.furini@univr.it 045 802 7950; Lab: 045 802 7043

Gregorio Enrico

Enrico.Gregorio@univr.it 045 802 7937

Guardavaccaro Daniele

daniele.guardavaccaro@univr.it +39 045 802 7903

Lampis Silvia

silvia.lampis@univr.it 045 802 7095

Marino Valerio

valerio.marino@univr.it 0458027227

Munari Francesca

francesca.munari@univr.it +39 045 802 7906

Pandolfini Tiziana

tiziana.pandolfini@univr.it 045 802 7918

Pezzotti Mario

mario.pezzotti@univr.it +39045 802 7951

Romeo Alessandro

alessandro.romeo@univr.it +39 045 802 7974-7936; Lab: +39 045 802 7808

Simonato Barbara

barbara.simonato@univr.it +39 045 802 7832; Lab. 7960

Speghini Adolfo

adolfo.speghini@univr.it +39 045 8027900

Tomazzoli Claudio

claudio.tomazzoli@univr.it

Torriani Sandra

sandra.torriani@univr.it 045 802 7921

Ugel Stefano

stefano.ugel@univr.it 045-8126451

Vettori Andrea

andrea.vettori@univr.it 045 802 7861/7862

Vitulo Nicola

nicola.vitulo@univr.it 0458027982

Zaccone Claudio

claudio.zaccone@univr.it +39 045 8027864

Zapparoli Giacomo

giacomo.zapparoli@univr.it +390458027047

Zenoni Sara

sara.zenoni@univr.it 045 802 7941

Zipeto Donato

donato.zipeto@univr.it +39 045 802 7204

Zivcovich Franco

franco.zivcovich@univr.it

Zoccatelli Gianni

gianni.zoccatelli@univr.it +39 045 802 7952

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.

CURRICULUM TIPO:
ModulesCreditsTAFSSD
12
B
(BIO/04)
9
A
(CHIM/06)
6
A
(FIS/07)
English B1 level
6
E
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S02690

Credits

12

Coordinatore

Franco Zivcovich

Scientific Disciplinary Sector (SSD)

MAT/05 - MATHEMATICAL ANALYSIS

Language

Italian

The teaching is organized as follows:

Matematica Mod. 1

Credits

6

Period

I semestre

Academic staff

Franco Zivcovich

Matematica Mod. 2

Credits

2

Period

I semestre

Academic staff

Roberto Chignola

Statistica

Credits

4

Period

I semestre

Academic staff

Roberto Chignola

Learning outcomes

Mathematics: This course aims at providing the students with the mathematical tools (set-theoretic and algebraic structures, differential and integral calculus in one or several real variables, ordinary differential equations) whose knowledge is indispensable for the achievement of the degree. A particular attention is paid to the concrete application of the learned notions. At the end of the course students should be able to use appropriately the mathematical language and the notions of the syllabus and furnish valid arguments in support of the solution of the proposed problems. Statistics: The aim of the course is to make the students acquainted with basic statistical ideas and mathematical methods and their applications in the correct planning of experiments, data sampling, analysis, and presentation. The course conjugates concepts of basic statistics and probability theory as well as applied mathematics with real situations as they emerge in a standard biotechnology laboratory. The students acquire appropriate skills to understand how biological systems work and more generally to cope with real-life problems in different applied scientific fields. At the end of the course the students are able to: - analyse experimental observations and prepare professional reports - appropriately plan experiments - autonomously acquire new skills in specific fields of applied statistics and mathematics.

Program

------------------------
MM: mathematics
------------------------
1. PRELIMINARIES.
a) Sets and operations with sets.
b) Numerical sets. Bounded and unbounded sets. Minimum, maximum, infimum and supremum of numerical sets.
c) Natural numbers N, integers Z, rationals Q, and reals R. Intervals. Distance.
d) Monomials, polynomials and polynomial decomposition.
e) Absolute values. Powers with natural, rational and real exponent. The polynomial functions x^a, irrational, exponential a^x, logarithmic.
f) Trigonometry.
g) Entire, rational, irrational, with absolute values and systems of equalities and inequalities.
h) Exponential, logarithmic and systems of inequalities.
i) Analytical geometry in the Cartesian plane: distances between points, lines, circumference, parabola, ellipse and hyperbola. Mutual positions and geometric problems.

2. FUNCTIONS.
a) Functions of real variable, plot, domain and image.
b) Compounded functions.
c) Inverse functions.
d) Monotonic functions.
e) Bounded and unbounded functions.
f) Maxima and minima, suprema and infima of functions.
g) Signs and zeros of a function.
h) Operations with plots, translations and symmetries.

3. LIMITS.
a) Distance and neighborhoods, right and left neighborhoods. Limit with functions. Continuity at one point. Elementary limits. Limits algebra. Limits of composed functions. Squeeze theorem. Indeterminate forms. Comparison between infinites and between infinitesimals. Horizontal, vertical, and obliquos asymptotes.
b) Continuous functions and their fundamental properties. Weierstrass theorem.

4. DERIVATIVES.
a) Definition of derivative at one points. Left and right derivative. Tangent line to a plot. Derivative function.
b) Derivatives of elementary functions. Derivation rules.
c) Derivability and continuity.
d) Critical points. Fermat theorem, Rolle theorem and Lagrange theorem. Implications of Lagrange theorem: derivable functions with null derivative, derivable functions with equal derivative, sign of first derivative and monotonicity intervals of a function. Detection and classification of relative maxima and minima through derivative's sign.
e) Second derivative, its sign and convexity.
f) Higher order derivatives. Local approximation of functions with polynomials. De l’Hopital theorem. Taylor's series and Taylor's theorem. Determine limits by using Taylor's theorem.

5. INTEGRALS.
a) Primitive functions (indefinite integrals). Elementary integrals. Definition of definite integral. Fundamental theorem of integral calculus.
b) Computing areas using integrals.
c) Overview of improper integrals on unlimited intervals.

6. DIFFERENTIAL EQUATIONS.
a) Definitions of differential equations (in normal and nonnormal form) and of order of a differential equation.
b) Solution and general solution of a differential equation. Examples of differential equations. Cauchy problem.

7. ALGEBRA LINEARE.
a) Vectors and vectors in R^n. Real valued matrices. Product between matrices.
and its properties. Linear systems in matrix form Ax = b. Solving linear systems with Gauss method.
b) Rank of a matrix. Determinant of square matrices. Rouché-Capelli's theorem. Cramer Teorema. Inverse of a square matrix.
c) Scalar product and its properties. Norm of a vector. Orthogonal vectors.

8. COMPLEMENTS
a) Bivariate functions, domain, detection and classification of critical points.
b) Complex numbers, operations with complex numbers, Euler formula.


------------------------
MM: statistics
------------------------
Each class introduces basic concepts of probability theory and applied statistics through combination of lectures and exercises. The exercises focus on the analysis of real experimental data collected in the teacher's lab or in other biotechnology labs. Lectures • brief introduction on the scientific method: the philosophical approach of Popper, Khun, and Lakatos and the concept of validation/falsification of hypotheses • variables and measurements, frequency distribution of data sampled from discrete and continuous variables, displaying data • elements of probability theory: definition, a brief history of probability, the different approaches to probability, the rules for adding and multiplying probabilities, Bayes' theorem • discrete probability distributions: the Binomial and the Poisson distributions and the limiting dilution assay with animal cells • continuous probability distributions: the concept of probability density, the Normal distribution and the Z statistics • statistical inference: the problem of deducing the properties of an underlying distribution by data analysis; populations vs. samples. The central limit theorem • the Student distribution and the t statistics. Confidence intervals for the mean. Comparing sample means of two related or independent samples • mathematical properties of the variance and error propagation theory • planning experiments and the power of a statistical test • the χ2 distribution and confidence intervals of the variance • goodness-of-fit test and χ2 test for contingency tables • problems of data dredging and the ANOVA test • correlation and linear regression The program follows the topics listed in the textbook up to chapter 17 (included) with the following extras: key aspects in probability theory, probability distributions in the biotechnology lab (practical examples), error propagation theory Reference textbook: Michael C. Whitlock, Dolph Schluter. Analisi Statistica dei dati biologici. Zanichelli, 2010. ISBN: 978-88-08-06297-0 Lecture slides are available at: http://profs.scienze.univr.it/~chignola/teaching.html

Examination Methods

------------------------
MM: mathematics
------------------------
The final exam is written and must be completed in 3 hours. Neither midterm tests nor oral exams will take place. The exam paper consists of 6 exercises. The total of the marks of the exam paper is 30. Any topic dealt with during the lectures can be examined. Students are not allowed to use books, notes or electronic devices during the exam. The evaluation of any exercise will take into consideration not only the correctness of the results, but also the method adopted for the solution and the precise references to theoretical results (e.g. theorems) taught during the lectures. The pass mark for the exam of the Mathematics module is 18.

------------------------
MM: statistics
------------------------
At the end of the course students are expected to master the basic concepts of probability theory and of validation/falsification of hypotheses, and to apply these concepts to the analysis of experimental data collected in a generic biotechnology laboratory. To pass the final written test, students are asked to solve 4 exercises within a maximum of 2 hours. The exercises concern the analysis of problems as they are found in a biotechnology laboratory. During the test, students are allowed to use learning resources such as books, lecture slides, handouts, but the use of personal computers or any other electronic device with an internet connection is not allowed. Eight points are assigned to the solution of each exercise and all points are then summed up. To pass their test students must reach a minimum score of 18 points.

The final score of the whole course in Mathematics and Statistics is calculated as the mean of the marks obtained by students in both tests.

Bibliografia

Reference texts
Activity Author Title Publishing house Year ISBN Notes
Matematica Mod. 1 M.Bramanti,C.D.Pagani,S.Salsa Analisi Matematica 1 Zanichelli 2009 978-88-08-06485-1
Matematica Mod. 1 Walter Dambrosio Analisi matematica Fare e comprendere Con elementi di probabilità e statistica Zanichelli 2018 9788808220745
Matematica Mod. 1 Dario Benedetto Mirko Degli Esposti Carlotta Maffei Matematica per scienze della vita Casa Editrice Ambrosiana. Distribuzione esclusiva Zanichelli 2015 9788808184849

Type D and Type F activities

Le attività formative in ambito D o F comprendono gli insegnamenti impartiti presso l'Università di Verona o periodi di stage/tirocinio professionale.
Nella scelta delle attività di tipo D, gli studenti dovranno tener presente che in sede di approvazione si terrà conto della coerenza delle loro scelte con il progetto formativo del loro piano di studio e dell'adeguatezza delle motivazioni eventualmente fornite.

 

I semestre From 10/1/20 To 1/29/21
years Modules TAF Teacher
Model organism in biotechnology research D Andrea Vettori (Coordinatore)
II semestre From 3/1/21 To 6/11/21
years Modules TAF Teacher
Python programming language D Vittoria Cozza (Coordinatore)
List of courses with unassigned period
years Modules TAF Teacher
Subject requirements: chemistry and biology D Not yet assigned
Subject requirements: basic mathematics and physics D Not yet assigned
LaTeX Language D Enrico Gregorio (Coordinatore)

Career prospects


Module/Programme news

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

Graduation

List of theses and work experience proposals

theses proposals Research area
Studio delle proprietà di luminescenza di lantanidi in matrici proteiche Synthetic Chemistry and Materials: Materials synthesis, structure-properties relations, functional and advanced materials, molecular architecture, organic chemistry - Colloid chemistry
Multifunctional organic-inorganic hybrid nanomaterials for applications in Biotechnology and Green Chemistry Synthetic Chemistry and Materials: Materials synthesis, structure-properties relations, functional and advanced materials, molecular architecture, organic chemistry - New materials: oxides, alloys, composite, organic-inorganic hybrid, nanoparticles
Stampa 3D di nanocompositi polimerici luminescenti per applicazioni in Nanomedicina Synthetic Chemistry and Materials: Materials synthesis, structure-properties relations, functional and advanced materials, molecular architecture, organic chemistry - New materials: oxides, alloys, composite, organic-inorganic hybrid, nanoparticles
Biologia e proprietà immunologiche delle cellule staminali fetali Various topics
Dinamiche della metilazione del DNA e loro contributo durante il processo di maturazione della bacca di vite. Various topics
Risposte trascrittomiche a sollecitazioni ambientali in vite Various topics
Studio delle basi genomico-funzionali del processo di embriogenesi somatica in vite Various topics

Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance is not mandatory. However, professors may require students to attend lectures for a minimum of hours in order to be able to take the module exam, in which case the methods that will be used to check attendance will be explained at the beginning of the module. 
Please refer to the Crisis Unit's latest updates for the mode of teaching.

Gestione carriere


Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.