Feynman path integrals
I anno: this short lecture series provides a very brief introduction to the Feynman Path Integral approach to quantisation, which represents a completely original and alternative axiomatic basis for quantum theories. While developed within the framework of non-relativistic quantum mechanics, the method is of particular importance (for its clarity and simplicity) in second or field quantisation. However, it finds useful applications in many other areas, as diverse as statistical mechanics and even, for example, financial analysis. Being limited to eight hours of front-on lectures, little attention is paid to mathematical rigour. In other words, a description of the physical basis and significance will be provided, together with some of the interesting applications of the technique of relevance to physics.  

Quantum technologies
I anno: the second quantum revolution is unfolding now, exploiting the enormous advancements in our ability to detect and manipulate single quantum objects and triggering the development of the different quantum technologies. The course, after a preliminary introduction to the principles of quantum information, is intended to give an introduction to the different quantum technologies for computing, simulation, communication, metrology, and machine learning, including quantum optics laboratory demonstration of quantum information protocols.  

Introduction to non-equilibrium statistical physics
II anno; classical statistical mechanics describes system at thermodynamic equilibrium. However, a wide range of natural systems, ranging from climate to virtually all living matter, are kept out of equilibrium by external driving and/or fluxes of energy which is constantly absorbed and dissipated through the system.
This course introduces some of the concepts employed in the study of macroscopic systems away from their state of thermodynamic equilibrium, mainly covering kinetic theory, stochastic processes and linear response. These constitute the principles to describe systems slightly perturbed out of equilibrium. Other selected topics may include active matter, pattern formation and systems with absorbing states.

 Non-Newtonian aspects of general relativity
I anno: this is a 20 hours course, where some specific aspects of the general relativistic dynamics will be discussed, compared to the analogous mechanisms in Newtonian gravity, when allowed. The main arguments included in the course are:
-Einstein equations and their comparison with Newtonian gravity: time dependence, non harmonicity, tensorial character, non linearity;
-Gravitational waves: generation and detection; weak and strong gravitational waves, effective one-body formalism;
-Dragging effects and weak and strong gravitomagnetism, with applications to the motion of massive bodies and galaxies.
However, modifications on the program can be considered on demand, in order to adapt the details of the lessons to the specific needs of the students.  

Complements of theoretical physics
II anno: this is a 20 hours course, with the aim of presenting some advanced topics in theoretical physics, related with the physics of the standard model and/or general aspects of theoretical physics. The main arguments proposed by the course are:
-Symmetries in quantum field theory: Lie groups, spontaneous symmetry breaking of global symmetries and local symmetries, symmetries in path integral formalism, anomalies.
-Feynman integrals in classical and quantum theories. Methods of computation of the Feynman integrals and related twisted cohomology;
-The geometry of the Standard Model of Particles and possible extensions to grand unification models.
According to the interests of the participating students, it is possible to consider a short introduction to all such arguments, or to develop more deeply one of them if needed.  

Fluorescence Methods in molecular biophysics
I anno: biophysics is a subject intrinsically at the interface between hard sciences and life sciences, and constitutes an ideal bridge within these two fascinating universes. In this teaching module the biophysical approach will be outlined, evidencing its specificities and originality with respect to classical biological subjects. The basics of molecular biology will be sketched in “pills”. In so doing, the students will be led to mature a deep comprehension of the intimate relationship between biomolecules structures and their biological function. Subsequently, some considerations will be driven about the peculiar thermodynamics of metabolic reactions. Binding reactions will be modelled in details evidencing their crucial role in metabolic regulation. The main concepts underlying the electronic state transition spectroscopy of molecules in solution will be recalled. The advantages of fluorescence will be discussed and several advanced fluorescence techniques will be presented and applied to the elucidation of binding equilibriums. Particularly, we will focus on time-resolved and fluctuation analyses methods. Further, we will correlate the conformational dynamics of biomolecules with benchmark spectral features, introducing fluorescence resonance energy transfer, both applied on molecular ensembles and at the single molecule level, as a powerful tool to characterize biomolecular structures and interactions. All the above topics will be tackled by relying both on examples extracted from the research experience of the teacher and on critical examination of seminal papers from the historical literature. At the end of the course, the students will inherit a comprehensive panorama of time-correlated single photon counting, fluorescence correlation spectroscopy and single-molecule fluorescence resonance energy transfer. Moreover, they will gain some insight on the molecular mechanisms governing life and on the physical laws on which they are based.

Fundamentals of machine learning
I e II anno: the course will cover the state-of-the art algorithms and knowledge about the supervised learning techniques .
In particular, those topics will be coveres:
i) Introduction to classifications problems and methods. Definition of Training Set, Test Set, Validation and metrics to assess the performances of classifiers
ii) Classification based on parametric model
iii) Classification based on non-parametric model . Decision Trees, Random Forests and linear classified will be presented with the necessary mathematical background and applications in physics/astrophysics.
iv) Classification with Support Vector Machine and Neural Networks
v) Introduction to Deep Learning techniques and architectures of deep neural networks for image classifications.
In order to fully exploit the lessons and the concepts given in the course plan, an introduction to data preparation, features selections and transformation (e.g. principal component analysis) will also be part of the lectures.  
Nota generale a tutti i corsi: gli studenti saranno tenuti a seguire almeno 60 ore di corso all'interno dell'offerta formativa, secondo un piano didattico approvato dal Collegio dei Docenti.

Numerical methods for astrophysics
I anno: the course "Numerical methods for astrophysics" is meant to introduce state-of-the-art numerical modelling techniques employed in astrophysics, and will cover 20 hours. In detail, the following topics will be covered:
- Newtonian dynamics and the N-body problem (grid-based vs particle-based methods)
- Advanced techniques for computational hydrodynamics: adaptive mesh refinement vs moving-mesh vs mesh-free methods, advantages and limitations
- High performance computing: code development and optimisation

Time series analysis for astrophysicists
I e II anno: time series are ubiquitous in astrophysics. This course is aimed at providing PhD students the main capabilities to extract physical information with state-of-the-art statistical inferences from the available datasets. We will refer to real science cases developed in the astrophysical literature, yet the discussed methodologies could be of definite interest to anyone involved in quantitative analysis of data in a temporal (or spatial) sequence in any field of modern physics, economy, engineering and social sciences.
At the end of the course students will be able to:
• carry out analysis of any statistical problem in a full Bayesian framework;
• properly model time series to derive meaningful statistical inferences about stationarity, short- and long-term memory behavior;
• deal with data irregularly spaced and/or affected by correlated noise;
• apply big-data techniques to carry out the analyses of typical large datasets obtained by modern astrophysical facilities.
More specifically, the main topics of the course are
• Time (and spatial) variability in astrophysics
• Time-domain analysis and auto-regressive processes
• Irregular sampling, Lomb-Scargle periodograms
◦ Case studies: AGN variability
• Advanced topics: non-parametric analysis
• Matching filters
◦ Case study: LIGO/Virgo gravitational wave signals
• Big-data, machine learning and “intelligent” systems for time-series analysis
◦ Case studies: spatial variability (CMB, large scale structure) 

School on Neuroimmune Pharmacology
I, II e III anno: la neuroimmunofarmacologia si colloca all'intersezione di farmacologia, immunologia e neuroscienze, caratterizzandosi dunque per un elevato grado di inter- e transdisciplinarietà e offrendo approcci terapeutici originali fondati sulla conoscenza dei complessi rapporti tra sistema nervoso e sistema immunitario. L'obiettivo prioritario di questa disciplina è l'identificazione di nuovi bersagli farmacoterapeutici e il riposizionamento di farmaci già disponibili per altre indicazioni grazie a una caratterizzazione dei loro effetti neuroimmunologici. La neuroimmunofarmacologia è una disciplina scientifica alquanto giovane ma in rapida e vivace crescita. Nel 1993 è stata fondata la Società di Neuroimmunofarmacologia (Society on Neuroimmune Pharmacology, SNIP - https://s-nip.org/), nel 2006 inizia la pubblicazione del Journal of Neuroimmune Pharmacology (Springer-Nature, https://link.springer.com/journal/11481), e nel 2008 viene pubblicata la prima edizione del volume Neuroimmune Pharmacology (JNIP, Springer), cui nel 2016 è seguita una seconda edizione aggiornata e estesa (https://amzn.to/2OmZniT). Questa disciplina non si limita allo studio delle malattie infiammatorie e autoimmuni del sistema nervoso (ad esempio, la sclerosi multipla e le neuropatie periferiche, classici ambiti di interesse della neuroimmunologia), bensì comprende processi neurodegenerativi quali la malattia di Parkinson e di Alzheimer, i disturbi correlati allo stress, le malattie cardiovascolari e in generale tutte quelle condizioni nelle quali processi infiammatori periferici e centrali coinvolgono e/o sono influenzati dal sistema nervoso.
La School è parte integrante dell'attività didattica e formativa del Corso di Dottorato. Anche grazie alla collaborazione delle società scientifiche patrocinanti, la School viene promossa in ambito internazionale.

School on Methodology, Ethics and Integrity in Biomedical Research
I, II e III anno: questa School nasce nell'ambito del corso di dottorato di ricerca in Medicina Clinica e Sperimentale e Medical Humanities, e vede l'adesione dei corsi di dottorato che in esso sono confluiti, e precisamente il corso in Farmacologia Clinica e Sperimentale e il corso in Medicina e Scienze Umane. Coerentemente con le finalità dichiarate del corso ("fornire le competenze metodologiche per progettare e realizzare attività di ricerca sperimentale e clinica in una prospettiva globale e sistemica che porti ad approfondire la riflessione anche sugli aspetti etici, e deontologici connessi alla ricerca sperimentale di base e applicata alla clinica e alla terapia, farmacologica e non farmacologica"), i docenti che hanno dato vita a quella proposta caratterizzata da forte innovatività e interdisciplinarietà intendono ora sviluppare un ulteriore strumento, integrato nell'offerta formativa post lauream, utile a perseguire l'obiettivo della promozione della cultura dell'integrità scientifica in biomedicina.
L'Università dell'Insubria si distingue nel panorama nazionale per una specifica attenzione a questi temi di grande attualità e portata globale, ancora quasi del tutto assenti nel dibattito politico-strategico della ricerca scientifica italiana. Il nostro Ateneo si è infatti dotato di un Codice Etico che - come nessun altro a nostra conoscenza tra le università italiane - affronta in maniera estremamente concreta temi quali il conflitto di interessi e l'integrità nella ricerca scientifica. A tale riguardo e ai fini di questo progetto di School, è di estremo rilievo l'esperienza del ciclo di seminari sul Codice Etico, promosso e organizzato dal gruppo di lavoro che ha redatto il Codice (del quale facevano parte alcuni dei proponenti di questa School), e che ha avuto notevole successo e attenzione anche grazie alla pubblicazione dei filmati in forma integrale sul web.

Giornata scientifica annuale del Corso
I, II e III anno: la ricerca biomedica è tra i fattori cruciali per il progresso e il benessere delle società moderne, e la professione del ricercatore biomedico è tra le più entusiasmanti dal punto di vista intellettuale.
Negli ultimi decenni, tuttavia, intraprendere una carriera di ricerca nel campo della biomedicina è diventato sempre più difficile, a causa soprattutto dell’aumento della competizione e della riduzione dei finanziamenti. Le pressione dovuta all’intensa competizione per i finanziamenti e per assicurarsi un posto di lavoro stabile nelle istituzioni accademiche sono tra i maggiori fattori che spingono i ricercatori a infrangere la loro integrità professionale adottando pratiche di ricerca discutibili, fino a commettere atti di vera e propria cattiva condotta scientifica.
Il Corso di Dottorato di ricerca in Medicina Clinica e Sperimentale e Medical Humanities intende rispondere alle esigenze formative dei giovani ricercatori fornendo conoscenze complete e aggiornate sui principi fondamentali, sulle metodologie e le pratiche della ricerca sperimentale preclinica e clinica in biomedicina.
L'annuale giornata scientifica, giunta nel 2022 all'ottava edizione (https://tinyurl.com/y8vuyxag) costituisce un appuntamento consolidato e di grande valore formativo per i dottorandi che apprendono in tal modo i fondamenti della comunicazione e della discussione in ambito scientifico congressuale. 

The axiomatic approach to risk measures
primo anno   The course covers the following topics:
1. Coherent risk measures
Definition of risk
Acceptable positions
Coherent risk
measures
Standard portfolio analysis
VaR
General dual representation for coherent risk measures
2. Optimization of static risk measures
Convex risk measures: definition, dual representation, examples
Minimizing VaR and CVaR
Optimization of convex risk measures
Quasiconvex risk measure: definition, dual representation, examples
Optimization of quasiconvex risk measures
3.Dynamic risk measures
Dynamic risk measures: definition, dual representation, examples
The notion of time consistency
Time consistent risk
measures: characterization in terms of the penalty function and
examples
Insegnamento impartito interamente in lingua inglese

Portfolio Allocation in Practice
primo anno   Topics Covered:
Lecture 1
1- Constructing the efficient frontier with only risky asset, using the components of market index.
2- Analyzing the impact of introducing lower and upper bounds on the on the efficient frontier.
3- Analyzing how the efficient frontier changes when introducing a risk free asset.
Lecture 2
1- Estimation methods for the input parameters in a portfolio selection problem.
2- Can we use market indices as market portfolios? Analyzing some results
3- Black-Litterman model (how to introduce active views)
Lecture 3
1- Different methods used to build a market index.
2- Portfolio selection using a CARA utility function and second order Taylor approximation.
3- Buy-and Hold rolling window strategy
4- Building the efficient frontier under uncertainty
Lecture 4
1- A back-testing procedure in case of portfolio selection.
2- In-sample and out-of-sample analysis
3- Sensitivity analysis on the impact of the risk aversion parameter on portfolio diversification.
Lecture 5
1- Drawback of the rolling-window strategy
2- Taking into account of the transaction costs
3- The impact of introducing higher moments on portfolio selection
Lecture 6
1- Introducing the network theory to portfolio
2- Presentation of the case studies by the students
Insegnamento impartito interamente in lingua inglese 

Applied econometrics: causality and policy evaluation
primo anno   Topics Covered:
1. Causal Inference: Definition, estimation, validity (4 hours)
- The Neyman–Rubin causal model: definition, assumptions and relations with other approaches;
- Regression, IV and Causality: identification and estimation problems;
- Brief introduction to R and STATA.
2. Estimating Causal Effects Using Experimental Data (4 hours)
- Randomized Controlled Experiments and Conditional Randomization;
- Heterogeneity in Treatment Effects and Imperfect Compliance;
- Relationship between ATE, ATT and LATE;
- Spill-overs/Externalities, External Validity and other problems with Randomized Experiments.
- Practical session: examples/take home with R and/or STATA.
3. Quasi-Natural Experiments and Observational Studies (4 hours)
- Quasi- or Natural Experiments;
- IV Approaches and Problems with Weak Instruments;
- Regression Discontinuity Designs: Sharp and Fuzzy RDDs.
- Practical session: examples/take home with R and/or STATA.
4. Selection on observables and matching (4 hours)
- Model dependence and matching;
- Assumptions: SUTVA, Conditional independence, Common support;
- Exact and approximate matching:
a) Propensity Score Matching (PSM);
- b) Coarsened Exact Matching (CEM);
- Practical session: examples/take home with R.
5. Difference-in-Differences estimator and other methods (4 hours)
- Difference-in-Differences estimator: assumptions and validity;
- Multiple groups and multiple periods;
- Practical session: examples/take home with R and/or STATA.
Insegnamento impartito interamente in lingua inglese 

Introduction to Macro Agent Based models 
primo anno   Topics covered:
1. Limits of standard Macro Models
• Delli Gatti, Domenico & Desiderio, Saul & Gaffeo, Edoardo & Cirillo, Pasquale & Gallegati, Mauro. (2011). Macroeconomics from the Bottom-up. 10.1007/978-88-470-1971-3. (Ch.1)
2. Complexity in Economics and ACE
• Tesfatsion, Leigh, (2006), Agent-Based Computational Economics: A Constructive Approach to Economic Theory, Staff General Research Papers Archive, Iowa State University, Department of Economics.
3. Macro Agent-Based Model
• Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046, February. (Chs.1 and 2)
4. AB macro models in R a trivial example
• Caiani, Alessandro & Russo, Alberto & Palestrini, Antonio & Gallegati, Mauro. (2016). Economics with Heterogeneous Interacting Agents: A Practical Guide to Agent-Based Modeling. 10.1007/978-3-319-44058-3. (ch.2)
5. An intro to social accounting and the flow of funds
• Eugenio Caverzasi & Antoine Godin, 2015."Post-Keynesian stock-flow-consistent modelling: a survey," Cambridge Journal of Economics, Oxford University Press, vol. 39(1), pages 157-187.
• Wynne Godley & Marc Lavoie, 2007."Monetary Economics," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-62654-6. (Ch.2)
6. Intro to Stock-Flow consistent modelling
• Michalis Nikiforos & Gennaro Zezza, 2017."Stock Flow Consistent Macroeconomic Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 31(5), pages 1204-1239, December.
7. SFC macro models in R a trivial example
• Wynne Godley & Marc Lavoie, 2007."Monetary Economics," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-62654-6. (Ch.3)
Insegnamento impartito interamente in lingua inglese

History of decision making
primo anno   History–and economic and business history are no exception–can be considered as the result of countless decisions taken by as many different actors, often intertwined with one another. The course aims at showing that all those decisions are taken in a context, a context characterised by uncertainty and by limits to the choices actors can make. And that, in this sense, path dependence and uncertainty may affect the main determinants of economic growth–innovation & technological development, entrepreneurship, and institutional change. The course analyses such issues by presenting some relevant cases in modern economic and business history.
Format
The course lasts 12 hours, divided into two parts:
1. Lectures, 8 hours
2. Students’ presentations, 4 hours
The first part consists of 4 classes of two hours each, during which the instructor introduces and gives the framework of the topic, focussing on some specific issues and examples. Students are expected to prepare the suggested readings and to lively participate to in class discussion
1. General introduction and path dependence
2. Path dependence affecting technological change and innovation
3. Institutional innovation choices and path dependence
4. State owned enterprises, a case study; by a visiting professor
The second part consists of one meeting during which students present a case study and discuss it with the class       SI   Insegnamento impartito interamente in lingua inglese  
6. Applied econometrics: Health Economics   15  
primo anno   The course provides students with an understanding of the most relevant micro-econometric techniques available to applied researchers, with a particular focus on the use of individual level data in health economics. The purpose of the course is to help students in selecting techniques suited both to their data and to their economic model and illustrate the skills required to put these techniques into practice.
The emphasis of the course is on applied work and on the illustration of the use of relevant computer software (STATA) applied to large-scale survey datasets. The course is computer based and is built around empirical case studies rather than general theory and the emphasis is on learning by example. Relevant methods are presented alongside the Stata code and empirical results are discusses in class. Practical applications of the methods are illustrated using data on health from, among others, the British Household Panel Survey (BHPS) and the WHO World Health Survey (WHS).
By the end of the course, participants should be able to:
• formulate empirical problems involving micro-data
• select appropriate econometric methods
• understand methods of estimation and be able to implement them, using appropriate software • construct usable datasets and know the limitations of the data
• interpret the results of the analysis
Course content
The course will cover the following topics:
• - survey design;
• - binary choice models and models for categorical data;
• - anchoring vignettes and hierarchical models;
• - count data regression;
• - models and methods for panel data
• - field experiments in health (with and without compatible incentives)
Insegnamento impartito interamente in lingua inglese

The economics of altruistic decisions and charitable giving 
primo anno   The lectures are divided into three parts.
Part I gives a brief introduction to the theory of charitable giving, focusing on the motivations and the incentives of donors.
Part II reviews the literature on laboratory experiments aimed at testing the predictions of the theoretical models illustrated in Part I. Part III presents some topics on the ongoing empirical research, based on laboratory and field experiments, examining how donors respond to various fundraising strategies adopted by charitable organizations. A throughout coverage of the issues can be found in the surveys by Andreoni (2006), Andreoni and Payne (2013), Vesterlund (2015).
Part II and III of the lectures illustrate and discuss a selection of research papers. For the final exam, each student is asked to make an oral presentation of one or two research papers at her/his choice. on charitable giving.
Insegnamento impartito interamente in lingua inglese

Behavioral economics and social preferences
primo anno   The course will cover selected topics in behavioural economics with a focus on other-regarding preferences, together with an overview of the experimental methodologies.
One of the core assumptions of neoclassical microeconomic models is selfishness. This assumption is useful in theoretical modelling. Yet, it fails at describing human behaviour in many economic scenarios, as observed in economic experiments or in the field. In this course we present a selection of behavioural models that replace this restrictive assumption by alternatives that have a better empirical foundation, like other-regarding preferences and belief-dependent preferences.
For each topic, a three-fold perspective is adopted: i) presentation of the theoretical model(s); ii) study of their impact on relevant economic phenomena; iii) methodological analysis of the most common experimental strategy adopted to test their behavioural implications.
Aim of the course: Students are expected to acquire an understanding of the most famous economic models describing humans’ sociality, which cover approximately one third of behavioural economic papers. Moreover, students will get familiar with the basic tools of experimental economics and are expected to acquire the ability to evaluate the accuracy of experimental designs set out to test economic models of social preferences.
Insegnamento impartito interamente in lingua inglese

Transport decision making and sustainability
primo anno   Aim and contents of the course
Transport has a key role for the socio-economic wellness of any society, but it is also one of the most important sources of pollution, other negative externalities, and greenhouse gas emissions, affecting the climate changes.
The course provides students with an understanding of the most relevant and recent topics reflecting latest research debate about transport sustainability and related models for the analysis, by applying an interdisciplinary approach, in the framework of Sustainable Development Approach (United Nations’ Agenda 2030 SDGs). The focus is on the impacts of the individual and organization decisions about mobility, on the transport sector transition and on public policies for sustainability.
Topics and lecturers:
- Transport externalities and sustainable transport policies (3h)- Elena Maggi, University of Insubria
- Sustainable Mobility Contribution of New Business Models and Technologies in the Transport Sector (3h)- Graham Parkhurst, University of West England Bristol-UK
- Active and sustainable mobility in later life (2h) – Daniele Crotti, University of Insubria
- Travel trends and behaviour for a sustainable future (6h), Kiron Chatterjee, University of West England Bristol-UK
Insegnamento impartito interamente in lingua inglese  

Biostatistics
primo anno   Course: Biostatistics
Teacher: Giorgio Binelli
Length: 2.5 CFU (20 hours)
Objective:
All fields of modern biology cannot be successfully approached without a knowledge of their statistical and biometrical aspects. It is thus necessary to provide the student with interlaced biological and statistical knowledge. The goal of this course is to make the students familiar with the statistical theory and terminology, so to understand the power and pitfalls of statistical analysis, with special emphasis on the planning of the experiments and the analysis of experimental data in the field of Life Sciences.
COURSE TOPICS
Basics of statistical analysis
o Why use Statistics. Populations and samples. Basics of probability. Random variables.
o Frequency distributions; what is a statistical test: power and protection of a test, Type I and Type II errors.
The most common statistical tests
o Quantitative and qualitative variables – which test?
o Some uses of the z variable.
o The χ2 test. Goodness-of-fit test and comparisons between proportions.
o The General Linear Model (GLM)
o Some uses of Student’s t
Other statistical tests
o The model of Analysis of Variance (ANOVA).
o One-Way ANOVA: the completely randomised and the randomised block designs. Two-way ANOVA.
o Linear regression and correlation models, parameters estimate in linear, multiple and curvilinear regression.          

Trends in Biomolecular Sciences 
primo anno   Course: Trends in Biomolecular Science
Teacher: shared among members of the PhD course
Length: 2.5 CFU (20 hours)
Objective:
Biomolecular science is a research area that profits from a strong multidisciplinary expertise on genetics, microbiology, molecular and cell biology, structural biology, biochemistry, synthetic and systems biology, etc. This course is aimed to provide advanced and updated knowledge through a theoretical and practical training oriented to provide the required skills for supporting the students in their biotechnological research projects (in the different fields of application).
COURSE TOPICS
Over the years, the course will focus on most recent trends in molecular and cell biology related to:
- Analytical methods
- Advanced cell biology
- Advanced cell biochemistry
- Biopharmaceuticals: from concept to production
- Biomolecules
- Biotransformation and biocatalysis
- Experimental design and research methodology
- Molecular genetics
- Molecular microbiology
- Molecular system biology

 Bioinformatics
secondo anno   Course: Bioinformatics
Teacher: Gianluca Molla
Length: 2.5 CFU (20 hours)
Objective:
Provide student with practical knowledge of the main bioinformatic processes used in protein investigation. All lessons will be held at the computer, in an informatics class. Most of procedures shown will be performed by the students themselves under the supervision of the teacher. Results will be discussed in a critical manner.
COURSE TOPICS
The course will cover the main topics of the bioinformatics approach to the study of the structure/properties of the proteins. In details:
1. The format of structural data and structural databases (PDB files, RCSB, PDBeChem, ...) and how to search and retrieve structural information
2. Basis of visualization of 3D structures and software for macromolecule visualization (PyMol, VMD, ...)
3. Prediction of structural properties of proteins (secondary structure, transmembrane regions, signal peptides, ...)
8. Discover the evolutionary history of a protein: build a phylogenetic tree, detect the most conserved positions and predict the sequence of ancestral proteins
4. Construction of models of the 3D structure of a protein (ab initio modelling, homology modelling)
5. Build models of variants in vitro - foldX - ProSAR
6. Prediction of binding of small ligands (e.g., drugs) to proteins by Automated Molecular Docking
7. Prediction of quaternary structure haddock, Z-DOCK
8. Simulation of protein flexibility in solution: The Molecular Dynamics approach          

Trends in Biomedical Sciences
secondo anno   Course: Trends in Biomedical Sciences
Teacher: shared among members of the PhD course
Length: 2.5 CFU (20 hours)
Objective:
This course is aimed to provide advanced knowledge on the cellular and molecular bases of pathological processes, on the more recent diagnostic procedures and therapeutic intervention modes using a multi-scale approach (from the molecule to the bed of the patient). The acquired knowledge (on metabolic processes, methodologies and analytical tools) will enable students to perform physiological and pathological studies, as well as to develop novel diagnostic and therapeutic approaches.
COURSE TOPICS
The course is intended to provide advanced theoretical knowledge in the continuously growing areas of biology applied to biomedical sciences and clinical research. In details:
- Advanced clinical pathology
- Advanced techniques in bioimaging
- Advanced treatments in neurodegenerative disorders
- Biomedical engineering and biomaterials
- Cell therapy
- Omics in biomedical science
- Molecular oncology
- Vascular diseases – diagnosis, therapeutic approaches and interventions

Transversal skills
primo anno
secondo anno
terzo anno   Course: Transversal skills
Teacher: shared among members of the PhD course and external professionals
Length: 2.5 CFU (20 hours)
Objective:
Despite the undisputable value of specialized skills, the transversal ones represent a main key to professional success, according to some scientific studies. Transversal or soft skills pertain to cognitive, creative, managerial, relational and communication areas. Not only they represent the “most wanted” competences indicated by the corporate sector, but they are also essential life skills. Acquiring these skills provides students with the basic knowledge, abilities and qualities required to translate competences into suitable behavior for organizational purposes and for their professional evolution and success.
COURSE TOPICS
The course will provide students transversal skills. The course is organized in three modules, provided in different years:
Module A: Information Literacy; Digital Literacy, Doctoral thesis and copyright; Open access; Collecting and processing information
Module B: Presentation skills; Problem solving; Results dissertation
Module C: Economy; Project Management; Patents & trade marks; European research programs         Il corso è diviso in 3 moduli (A, B e C) e che vengono erogati sui tre anni (al I, II e III, rispettivamente. I dottorandi potranno scegliere tra le attività proposte nei vari moduli per acquisire i CFU richiesti.