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. 2019/2020

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, 2019 Jan 31, 2020
II semestre Mar 2, 2020 Jun 12, 2020
Exam sessions
Session From To
Sessione invernale d'esame Feb 3, 2020 Feb 28, 2020
Sessione estiva d'esame Jun 15, 2020 Jul 31, 2020
Sessione autunnale d'esame Sep 1, 2020 Sep 30, 2020
Degree sessions
Session From To
Sessione estiva di laurea Jul 17, 2020 Jul 17, 2020
Sessione autunnale di laurea Oct 13, 2020 Oct 13, 2020
Sessione autunnale di laurea - Dicembre Dec 9, 2020 Dec 9, 2020
Sessione invernale di laurea Mar 10, 2021 Mar 10, 2021
Holidays
Period From To
Festa di Ognissanti Nov 1, 2019 Nov 1, 2019
Festa dell'Immacolata Dec 8, 2019 Dec 8, 2019
Vacanze di Natale Dec 23, 2019 Jan 6, 2020
Vacanze di Pasqua Apr 10, 2020 Apr 14, 2020
Festa della Liberazione Apr 25, 2020 Apr 25, 2020
Festa del lavoro May 1, 2020 May 1, 2020
Festa del Santo Patrono May 21, 2020 May 21, 2020
Festa della Repubblica Jun 2, 2020 Jun 2, 2020
Vacanze estive Aug 10, 2020 Aug 23, 2020

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 N P R S T U V Z

Assfalg Michael

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

Astegno Alessandra

alessandra.astegno@univr.it 045802 7955

Badino Massimiliano

massimiliano.badino@univr.it +39 045 802 8459

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

Boscaini Maurizio

maurizio.boscaini@univr.it

Buffelli Mario Rosario

mario.buffelli@univr.it +39 0458027268

Cecconi Daniela

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

Chignola Roberto

roberto.chignola@univr.it 045 802 7953

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

Di Pierro Alessandra

alessandra.dipierro@univr.it +39 045 802 7971

Dominici Paola

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

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

Molesini Barbara

barbara.molesini@univr.it 045 802 7550

Munari Francesca

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

Nardon Chiara

chiara.nardon@univr.it

Pandolfini Tiziana

tiziana.pandolfini@univr.it 045 802 7918

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

Torriani Sandra

sandra.torriani@univr.it 045 802 7921

Ugel Stefano

stefano.ugel@univr.it 045-8126451
Foto personale,  July 18, 2012

Vallini Giovanni

giovanni.vallini@univr.it 045 802 7098; studio dottorandi: 045 802 7095

Vettori Andrea

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

Vitulo Nicola

nicola.vitulo@univr.it 0458027982

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

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
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

Chiara Pintossi

Scientific Disciplinary Sector (SSD)

MAT/05 - MATHEMATICAL ANALYSIS

Language

Italian

The teaching is organized as follows:

Matematica Modulo 1

Credits

6

Period

I semestre

Academic staff

Chiara Pintossi

Matematica Modulo 2

Credits

2

Period

I semestre

Academic staff

Chiara Pintossi

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 methods and their ap-plications in the correct planning of experiments, data sampling, analysis, and presentation. The course conjugates con-cepts of basic statistics and probability theory 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.

Program

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MM: matematica
------------------------
1. PRELIMINARY. a) Sets and operations on sets. b) Natural numbers N, integers Z and rational Q c) Real numbers. Operations and arrangement in R. Sets of limited or unlimited real numbers. Upper extremity and lower extremity of sets of real numbers. Intervals. Distance. d) Polinomial and irrational equations and inequalities, with modules and systems e) Analytical geometry in the Cartesian plane: distances between points, line, circumference, parabola, ellipse and hyperbola. Mutual positions and geometric problems 2. ELEMENTARY FUNCTIONS. a) Real functions of real variable, graph, domain, image. Composition of functions. Inverse function. Monotone functions. Limited functions and unlimited functions. Maxima and minima. Upper and lower extremes of functions. Sign and zeros of a function. Diagram operations: translations, symmetries. b) Absolute values. Powers with natural, rational and real exponent. The polynomial functions x ^ a, irrational, exponential a ^ x, logarithmic. The trigonometric functions. c) Algebric, exponential and logarithmic inequalities, systems of inequalities. 3. CONTINUOUS LIMITS AND FUNCTIONS. a) Distance and surroundings, right and left surroundings. Function limits. Continuity in one point. Elementary limits. Algebra of limits. Limits of compound functions. Comparison theorem. Some indeterminate forms. Comparison between infinites and infinitesimal. Horizontal, vertical, oblique asymptotes. b) Continuous functions and their fundamental properties. Theorem of the zeros. Weierstrass theorem. . 4. DERIVATIVES AND APPLICATIONS. a) Definition of derivative in a point. Right derivative and left derivative. Straight tangent to the graph. Derived function. Derivatives of elementary functions. Rules of derivation of sum, product, quotient, compound function, inverse function. Derivability and continuity. Relative maximum and minimum points. Fermat, Rolle and Lagrange theorems. Consequences of the Lagrange theorem: derivable functions with null derivative, derivable functions with the same derivative, sign of the first derivative and intervals of monotony of the function. Search for points of maximum or minimum relative through the sign of the derivative. Second derivative, its sign and convexity. b) Qualitative study of the graph of a function. c) Subsequent derivatives. Local approximation of functions with polynomials. Theorems of De l'Hospital. Taylor's polynomial and Taylor's theorem. Use of the theorem for determining limits. 5. INTEGRAL. a) Primitive functions (indefinite integrals). Elemental integrals. Definition of definite integral. Fundamental theorem of integral calculus. b) Calculation of areas through the use of integrals. c) Overview of improper integrals on unlimited intervals. 6. DIFFERENTIAL EQUATIONS. Definitions of differential equation (in normal and non-normal form) and of order of a differential equation. Solution and general solution of a differential equation. Examples of differential equations. Cauchy problem. 7. LINEAR ALGEBRA. a) Geometric vectors. Vectors in R ^ n. Matrixes with real coefficients. Produced between matrices and its property. Linear systems in matrix form Ax = b. Systems resolution with the Gauss method. b) Rank (or characteristic of A). Determinant of square matrices. Rouché-Capelli theorem. Cramer's theorem. Inverse of a square matrix. c) The scalar product and its properties. Standard (or form) of a vector. Orthogonal vectors. Elements of analytical geometry. Vector product in R ^ 3.
------------------------
MM: statistica
------------------------
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: matematica
------------------------
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 mark 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: statistica
------------------------
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 weighted mean of the marks obtained by students in both tests by taking into account the number of credits assigned to each course as weights: final grade = (2/3) x1 + (1/3) x2 where x1 and x2 are the marks obtained by students in their tests of Mathematics and Statistics, respectively.

Bibliografia

Reference texts
Activity Author Title Publishing house Year ISBN Notes
Matematica Modulo 1 Walter Dambrosio Analisi matematica Fare e comprendere Con elementi di probabilità e statistica Zanichelli 2018 9788808220745
Matematica Modulo 1 Guerraggio, A. Matematica per le scienze con MyMathlab (Edizione 2) Pearson 2014 9788871929415
Matematica Modulo 1 Dario Benedetto Mirko Degli Esposti Carlotta Maffei Matematica per scienze della vita Casa Editrice Ambrosiana. Distribuzione esclusiva Zanichelli 2015 9788808184849
Matematica Modulo 1 Sergio Invernizzi Maurizio Rinaldi Federico Comoglio Moduli di matematica e statistica Con l'uso di R Zanichelli 2018 9788808220714
Matematica Modulo 2 Walter Dambrosio Analisi matematica Fare e comprendere Con elementi di probabilità e statistica Zanichelli 2018 9788808220745
Matematica Modulo 2 Guerraggio, A. Matematica per le scienze con MyMathlab (Edizione 2) Pearson 2014 9788871929415
Matematica Modulo 2 Dario Benedetto Mirko Degli Esposti Carlotta Maffei Matematica per scienze della vita Casa Editrice Ambrosiana. Distribuzione esclusiva Zanichelli 2015 9788808184849
Matematica Modulo 2 Sergio Invernizzi Maurizio Rinaldi Federico Comoglio Moduli di matematica e statistica Con l'uso di R Zanichelli 2018 9788808220714
Statistica Michael C. Whitlock, Dolph Schluter Analisi Statistica dei dati biologici Zanichelli 2010 978-88-08-06297-0

Type D and Type F activities

I semestre From 10/1/19 To 1/31/20
years Modules TAF Teacher
Python programming language D Maurizio Boscaini (Coordinatore)
Model organism in biotechnology research D Andrea Vettori (Coordinatore)
II semestre From 3/2/20 To 6/12/20
years Modules TAF Teacher
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
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.