Courses of Academic Year 2020/2021

A preliminar list of the didactic offer of the ICT doctorate follows. The list will be completed and updated during the year.
Seminars on Bibliometrics and research Evaluation will be offered by UNIMORE for all the doctorate courses.

Uncertainty Quantification with Applications in Science and Engineering

Lecturer: Prof. Clemens Heitzinger (TU Wien)
Clemens Heitzinger received his master's degree (Dipl.-Ing.) in mathematics and his PhD degree (Dr. techn.) in technical sciences with honors both from TU Vienna. He was a visiting researcher in the Department of Mathematics and Statistics at Arizona State University, a research associate in the School of Electrical and Computer Engineering at Purdue University, and a senior research associate in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. In 2015, he returned to TU Vienna as an Associate Professor in the Department of Mathematics. He is also an Adjunct Professor in the School of Mathematical and Statistical Sciences at Arizona State University. He was awarded the START Prize, Austria's most prestigious award for young scientists, by the Austrian Science Fund (FWF) in 2013. His research interests are stochastic partial differential equations, uncertainty quantification, Bayesian PDE inversion, and reinforcement learning with applications for example in nanotechnology and medicine.


Monday, 22 Feb 10:00–11:30
Tuesday, 23 Feb 10:00–11:30
Wednesday, 24 Feb 10:00–11:30
Thursday, 25 Feb 10:00–11:30
Friday, 26 Feb 10:00–11:30

Monday, 1 March 14:00–15:30
Tuesday, 2 March 14:00–15:30
Wednesday, 3 March 14:00–15:30


In recent years, uncertainty quantification (UQ) has expanded the scope of modeling and simulation from deterministic partial differential equations to stochastic partial differential equations and to Bayesian methods for inverse or parameter-estimation problems. In this course, the most important equations and numerical methods of uncertainty quantification relevant to modeling and simulation in engineering and especially in nanoscience and nanotechnology are presented. Regarding the model equations, there is a focus on (charge) transport problems.

We start with deterministic partial differential equations and recapitulate some of their properties that we will build on when considering their stochastic versions. The theory of and state-of-the-art methods for the numerical solution of stochastic partial differential equations are presented. (Stochastic) homogenization for multiscale problems is discussed as well. Bayesian inversion is a strong UQ tool for calculating the probability distributions of unknown parameters or other quantities of interest from models and measurements. Throughout the course, motivating real-world applications, mostly from nanoscience and nanotechnology and from tomography, are discussed.

Lecture notes will be distributed as a PDF file (ca. 300 pages).

At the end of the course, you will have acquired state-of-the-art knowledge about the theory of important model equations, about the most important methods for their numerical solution, and how unknown parameters or other quantities of interest can be calculated when measurements are available. These ideas and methods can be applied to many other applications and model equations in other areas.

• Deterministic transport equations:

– Poisson equation,
– Poisson-Boltzmann equation,
– Boltzmann transport equation,
– drift-diffusion equations.
• Homogenization problems:
– homogenization of elliptic equations/operators,
– two-scale convergence.
• Stochastic transport equations:
– stochastic drift-diffusion-Poisson system,
– multi-level Monte-Carlo methods,
– optimal multi-level Monte-Carlo methods.
• Bayesian inversion:
– measure-theoretic framework,
– Markov-chain Monte-Carlo methods,
– delayed-rejection adaptive-Metropolis (DRAM) algorithm,
– optimal Bayesian inversion.
• Selected applications in engineering and nanotechnology:
– nanoscale field-effect sensors,
– tomography.

Not yet defined

CFD: 3

Data Analytics

Lecturer: Dr. Paolo Missier

Lunedi 26 aprile. 15-17
Mercoledi 28 15-17 e 17:30-18:30
Venerdi 30 10-12:30

The aim of the course is to introduce students to two important elements of data engineering technology that makes it possible to extract valuable knowledge from “Big Data”, namely distributed data processing using the MapReduce framework, and data analytics on large graphs. In each case, the course will first introduce fundamental theoretical and architectural concepts, and then present technology for using MapReduce and querying large graphs, respectively. For MapReduce, we present examples of algorithms that can be successfully parallelised and thus are able to take advantage of distributed data architectures, and we will suggest practical exercises using the Spark and Hadoop technology stack. Regarding graph analytics, we focus on community detection algorithms as an example, and we will use the Neo4J graph DBMS along with the Graph Data Science library for practical exercises.

Not yet defined

CFD: 4

Academic English Workshop I

Lecturer: Silvia Cavalieri


giorno 16/6 ore 10:00-13:00 Microsoft Teams

giorno 18/6 ore 10:00-13:00 Microsoft Teams

giorno 21/6 ore 10:00-13:00 Microsoft Teams

giorno 1/7 ore 10:00-13:00 Microsoft Teams

The workshop aims at giving an overview of the linguistic conventions adhered to by the English-speaking academic community, focussing on aspects such as the structure of research articles, the writing of abstracts and the preparation of conference presentations. More specifically, the workshop will try to raise the participants' awareness of the rhetorical and discourse patterns characteristic of academic English, introducing them to the skills required to produce texts which are accurate both from a grammatical and a stylistic point of view. Ample opportunities will be given for practice, both in the analysis and the production of texts. In particular, special attention will be given to argumentative writing techniques and to special genres such as the abstract and conference presentations.

Written assessment: abstract writing; oral assessment: conference presentation

CFD: 3

Biosensing with advanced electronic devices

Lecturer: Prof. Muhammad Alam Ashraf (Purdue University, USA)

Professor of Electrical and Computer Engineering
Purdue University
School of Electrical and Computer Engineering
Hall for Discovery & Learning Research
207 S. Martin Jischke Dr.
West Lafayette, Indiana 47907-1971

Muhammad Ashraful Alam is a Professor of Electrical and Computer Engineering where his research and teaching focus on physics, simulation, characterization and technology of classical and emerging electronic devices. From 1995 to 2003, he was with Bell Laboratories, Murray Hill, NJ, where he made important contributions to reliability physics of electronic devices, MOCVD crystal growth, and performance limits of semiconductor lasers. At Purdue, Alam’s research has broadened to include flexible electronics, solar cells, and nanobiosensors. He is a fellow of the AAAS, IEEE, and APS and received the 2006 IEEE Kiyo Tomiyasu Award for contributions to device technology.


May 2021

Lectures (of 1.5 hours each)

Part 1: Introduction to Nanobiosensors

Lecture 1: What is nanobiosensors, anyway?

Lecture 2: Basic concepts: Biomolecules, Analyte density, diffusion distances

Lecture 3:Basic concepts: Types of biosensors, geometry of biosensing

Part 2: Setting Time

Lecture 4: Response time of classical nanobiosensors

Lecture 5: Response time of complex nanobiosensors

Lecture 6: Beating the diffusion limit by biobarcode sensors

Lecture 7: Beating the diffusion limit by Droplet Spectroscopy

Lecture 8: Beating the diffusion limit by analyte flow

Lecture 9: Settling time vs. first passage time

Part 3: Sensitivity

Lecture 10:Sensitivity and types of biosensors

Lecture 11:Potentiometric biosensors

Lecture 12:On charge screening of cylindrical sensors

Lecture 13:ISFET as a pH-meter

Lecture 14:Origin of charges in a biomolecule

Lecture 15:How to beat screening

Lecture 16:Amperometric Sensor – An introduction to glucose sensors

Lecture 17:Amperometric Sensors – Michaelis-Menton equation

Lecture 18:Beating the diffusion limit in an amperometric DNA sensors

Lecture 19: Elements of an Cantilever sensor

Lecture 20:Cantilever sensors and its nonideal response

Lecture 21:Nonlinear nanobiosensing by a Flexture-FET

Part 4:Selectivity

Lecture 22: Selectivity: energetics of molecular recognition

Lecture 23: Selectivity: Spatial distribution of random sequential absorption

Lecture 24: When all else fail: Tag, filter, and amplify

Lecture 25:An information theory perspective on selectivity

Lecture 26:Physics of Ion-selective fuel-cell based glucose sensors

Lecture 27:Physics of Ion-selective sweat-sensors

Part 5:Putting them together

Lecture 28:Genome sequencing by Ion Torrent – Part 1

Lecture 29:Genome sequencing by Ion Torrent – Part 2

Lecture 30:Introduction to Microfluidic and paper based sensors.

Lecture 31: Conclusions: Looking back and looking forward

The course will provide an in-depth analysis of the origin of extra-ordinary sensitivity, fundamental limits, and operating principles of modern nanobiosensors. The primary focus will be physics of biomolecule detection in terms three elementary concepts: response time, sensitivity, and selectivity. And, we will use potentiometric, amperometric, and cantilever-based mass sensors to illustrate the application of concepts for specific sensor technologies. Roughly speaking Lectures 1- 5, 9, 10-15, 22, 23 cover the fundamental topics of sensing, the others complement the scenario with application examples.

Not yet defined

CFD: 4

Power electronics converters for automotive applications: trends and challenges

Lecturer: Prof. Giampaolo Buticchi, The University of Nottingham Ningbo China,

June/July 2021

Module 1 (4h) Multi-level converters, an automotive prospective for high speed drive applications
Module 2 (4h) High performance DC/DC converters for automotive applications
Module 3 (4h) Potential of Active thermal control for reliability improvement of automotive drives.

Not yet defined

CFD: 3

The basic principles of project management

Lecturer: Massimo Bertolini

to be defined

The course aims at providing the theoretical and practical fundamental knowledge of project management as a required tool for the design and the development of a project. The topics will be covered within the framework of PMBOK (Project Management Body of Knowledge) of PMI (Project Management Institute).

Drafting of a short essay, that will be presented and discussed by the students

CFD: 3

Bibliographic research, scientific writing and dissemination: tools, techniques and strategies

Lecturer: Simona Assirelli, Michele Pola

to be defined


Not yet defined

CFD: 3

The “Wide” Advantage in Power Electronics

Lecturer: Alessandro Chini


12, 15, 19 e 22 ottobre dalle ore 9 alle 12.

The course will be online with Microsoft Teams on the PhD team

Aim of this course is to introduce students to the performance improvement that power electronics circuits can achieve by taking advantage of the “wide-band-gap” semiconductor technologies. Gallium-Nitride and Silicon-Carbide based devices recently entered the market but they are expected to replace in a near future the conventional Silicon-based devices that represent nowadays the mainstream technology. The course will cover the following topics (total of 12hours/3CFU) 1) Role of efficiency in power conversion circuits 2) Operating concept of switching converters 3) Semiconductor switches and their figure of merits 4) Example of applications comparing the performances of Silicon, Gallium-Nitride and Silicon-Carbide based semiconductor devices

test a risposta multipla di circa 30 minuti

CFD: 3

Academic English Workshop II

Lecturer: Silvia Cavalieri


giorno 20/9 ore 10:00-13:00 sala master mo27

giorno 21/9 ore 10:00-13:00 aula P2.3

giorno 27/9 ore 10:00-13:00 sala riunioni 1 piano MO27

giorno 28/9 ore 10:00-13:00 sala riunioni 1 piano MO27

The main objective of the workshop is to provide PhD students with the knowledge of the rhetorical and linguistic models that characterize the English academic language. The seminar intends to introduce language and stylistic tools t to write accurate texts. In particular, the AEWII will focus on some specific written genres of the academia, i.e. doctoral theses and posters and Phd students will have extensive opportunities to practice both with text analysis and with their production. Special attention will be given to argumentative techniques of posters ‘ oral presentations.

written assessment: writing a paper introduction; oral assessment: poster presentation.

CFD: 3

Controllo ottimo vincolato (Constrained optimal control)

Lecturer: Prof. Paolo Falcone

9-10-12 Feb 4 hours: 11-13 14-16

Il corso introduce alla teoria del controllo ottimo predittivo (Model Predictive Control), partendo dalla formulazione del problema di controllo ottimo con costo quadratico per sistemi lineari (controllo LQ). I principali risultati di stabilita’ e di persistent feasibility verranno illustrati utilizzando esempi numerici e applicazioni in ambito automotive e Matlab toolbox quali MPT e MPC toolbox.

Not yet defined

CFD: 3

Corsi Di Formazione Complementare Per Dottorandi E Assegnisti Ediz. 2020/2021

Lecturer: Barbara Rebecchi, Ferdinando Di Maggio, Federica Manzoli, Nadja Seding, Giulia Catellani, Valeria Bergonzini, Valeria Goldoni

See program details .
dal 22 marzo sarà disponibile il CORSO DI FORMAZIONE COMPLEMENTARE PER DOTTORANDI e ASSEGNISTI Edizione 2020 - 2021, in modalità online. Come vedete dal programma allegato, sono previste 4 sessioni modulari costituite da lezioni asincrone ed alcune sessioni interattive “live”. Al termine di ogni “corso” in modalità asincrona sarà richiesta al/alla partecipante la compilazione di un questionario di verifica.
Accanto alle lezioni asincrone, i/le frequentanti avranno la possibilità di fruire di 7 lezioni interattive, secondo la seguente agenda (per cui NON è richiesta la compilazione di questionari di verifica):
- 29 marzo 2021 ore 9.30 - 11.00 - L’ecosistema dell’Innovazione in Emilia-Romagna - Luca Piccinno – ArtER;
- 31 marzo 2021 ore 14.00 - 16.00 - Esperienze di Open Innovation nell'Ecosistema emiliano-romagnolo Prof. Bernardo Balboni – Dipartimento di Economia Marco Biagi, Irene Comiti – ArtER, Alain Marenghi – ArtER;
- 01 aprile 2021 ore 9.30 - 10.15 - L’ecosistema dell’Innovazione in Emilia-Romagna - " Soft Skills " - Luca Piccinno – ArtER;
- 01 aprile 2021 ore 10.30 - 11.15 - L’ecosistema dell’Innovazione in Emilia-Romagna - " Design Thinking" - Luca Piccinno – ArtER;
- 26 aprile 2021 ore 10.00 – 12.00 - Bibliometria, valutazione della qualità, Open access, Ufficio bibliometrico - Andrea Solieri e Simona Assirelli - Ufficio bibliometrico di ateneo – SBA;
- 3 maggio 2021 ore 11:00-13:00 - Comunicare la ricerca, da Galileo alla citizen science - Federica Manzoli – Direzione ricerca, trasferimento tecnologico e terza missione (DRTTTM);
- 3 maggio 2021 ore 14:00-15:00 - Progettare la ricerca in Europa: valorizzazione dei risultati - Dott.ssa Nadja Saendig – Direzione ricerca, trasferimento tecnologico e terza missione (DRTTTM);
N.b: La frequenza delle sessioni live non è obbligatoria, ma fortemente consigliata.
Per accedere alle lezioni asincrone i partecipanti dovranno collegarsi alla piattaforma e-learning di ateneo, autenticandosi nella schermata dedicata.
Per le sessioni in modalità sincrona: le lezioni interattive “live” si terranno attraverso la piattaforma Microsoft Teams. Il Corso si Microsoft Teams è disponibile program details .

La valutazione si basa sulla somministrazione di questionari, compilabili soltanto dopo aver visualizzato interamente le videolezioni. Per poter ottenere l’attestato di partecipazione i frequentanti dovranno rispondere correttamente almeno al 50% delle domande. Al termine dell’erogazione del corso, l’ufficio ricerca internazionale estrarrà i report delle presenze e valutazioni direttamente dal portale Dolly. Successivamente provvederemo ad inoltrare a ciascun corso o scuola di dottorato i dettagli dei rispettivi partecipanti.

CFD: 6