phd in data science italy

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Phd in DATA SCIENCE

• DATA SCIENCE

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PhD in Data Science and Computation

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Application deadline: Aug 22, 2023 at 11:59 PM (Expired)

Second PhD Call for Applications, with scholarships funded by NRRP and other funds

• Call for applications
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Enrolment: From Sep 21, 2023 to Sep 28, 2023

Doctoral programme start date: Nov 01, 2023

Application deadline: Jun 20, 2023 at 11:59 PM (Expired)

PhD Call for Applications, with scholarships funded by NRRP and other funds

Enrolment: From Aug 01, 2023 to Aug 22, 2023

• Quantitative Finance and Economics
• Materials and Industry 4.0
• Genomics and bioinformatics
• Personalised medicine
• Hardware and Infrastructure
• Machine learning and deep learning
• Computational physics
• Computational chemistry
• Big Data, Smart Cities & Society

Call for further PhD Positions Appointed by Rectoral Decree n. 1066/2023 Prot. n. 0226313 of 04/08/2023

* The following shall take part in the work of the Examination Board as expert members for positions linked to specific research topics:

Lorenzo Cavicchi, Alberto Rigenti - Automobili Lamborghini Spa

Paolo Uva - Istituto Giannina Gaslini

Maurizio Ortali - CINECA

Call for applications Appointed by Rectoral Decree n. 709/2023 Prot. n. 149534 of 02/06/2023

This course aims to train students who will be ultimately capable of carrying out academic and industrial research activities, both basic and applied, in the fields of computer sciences, data analytics, and high-performance computing. These skills may be applied to several research domains, including economy, medicine, industry 4.0, physics, chemistry, etc. In this respect, each Ph.D. student during the course is expected to produce original and significant results in terms of scientific publications and/or innovative applications, starting from expertise, which is mandatory for fully understanding such approaches and their applications. Therefore, training will be in the following basic fields: mathematics, statistics, computer science, data analysis, computational physics and chemistry, bioinformatics, etc.).

At the beginning of the course, for each PhD student a supervisor is identified who supports him/her throughout the three-year duration of the program. During the first two years of the program, the cultural background of the PhD student is expected to be integrated, expanded and deepened according to a personalised learning plan, prepared by the student in agreement with the supervisor, and communicated to the Coordinator for information. The plan foresees the achievement of at least 150 hours of courses through attendance to lectures and/or fulfilment of required final examinations, of which at least 50% must be selected from the list of courses as part of the PhD offer, and the remaining portion to be composed by supervisors’ suggestions. At the end of the first year, the “Collegio dei Docenti” (i.e. Faculty Board) approves the admission to the following year: PhD students are encouraged to have finalized a good portion of the learning plan foreseen in the first two years, although there are no requirements on this that would block the admission to the second year; the delivery of a research topic proposal for the final thesis will be required. By the end of the second year, the PhD student must have entirely completed the learning plan and must give a public presentation on the progress of the thesis work and the results achieved up to that point; based on this, the Faculty Board approves the admission to the following year. Finally, the Faculty Board decides on the admission to the final examination, based on the comments of the reviewers and any revision of the thesis. The Faculty Board may authorise a PhD student to spend periods in Italy at Universities, research centres, or companies. It is mandatory for a PhD student to spend a period of at least 3 months abroad.

During the first two years of the program, the cultural background of the PhD student is expected to be integrated, expanded and deepened according to a personalised learning plan, prepared by the student in agreement with the supervisor (the supervisors are responsible of advising the PhD students and create an adequate training plan), and communicated to the Coordinator for information. The plan foresees the achievement of at least 150 hours of courses through attendance to lectures and/or fulfilment of required final examinations (with methods to be agreed with the course's teacher), of which at least 50% must be selected from the list of courses as part of the PhD offer, and the remaining portion to be composed by supervisors’ suggestions. By the end of the second year, the PhD student must have completed the personalised learning plan and must report on the progress of the thesis. The thesis will be discussed at the end of the third and final year.

The course is the product of the collaboration of international institutes and universities in the field of data science, such as  Alma Mater Studiorum University of Bologna, Cineca, the Italian Institute of Technology and INFN. Having undergone several changes over the years, the international characteristics of the course are still being designed and developed and will emerge according to the ability of each member Faculty of professors to activate and finalize agreements for the exchange of students, both incoming and outgoing, with universities and international research institutes of high scientific and academic level. We also plan to release joint degrees in collaboration with foreign universities. The remarkable academic and scientific profile of all the members of the Faculty at an international level is evident through the indexes that measure their scientific production.

Original, relevant and significant research results are due by the end of the Course that can take different forms including for example: scientific publications, system and software design, realization and production, and any kind of  innovative applications specializing on a broad gamut of topics, such as for example: Quantitative Finance and Economics; Materials and Industry 4.0; Genomics and bioinformatics; Personalised medicine; Hardware and Infrastructure; Machine learning and deep learning; Computational chemistry & physics; Big Data, Smart Cities & Society.

Daniele Bonacorsi

Dipartimento di Fisica e Astronomia "Augusto Righi" - DIFA

Viale Berti Pichat 6/2 Bologna (BO)

[email protected]

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(in collaboration with scuola normale superiore, sant’anna school, university of pisa and national research council).

The Program

The PhD in Data Science is aimed at educating the new generation of researchers that combine their disciplinary competences with those of a “data scientist”, able to exploit data and models for advancing knowledge in their own disciplines, or across diverse disciplines. To this purpose, the PhD in Data Science develops a mix of knowledge and skills on the methods and technologies for the management of large, heterogeneous and complex data, for data sensing ( how to harvest data ), for data analysis and mining ( how to make sense of data ), for data visualization and storytelling ( how to narrate data ), for understanding the ethical issues and the social impact of Data Science. The PhD students will have the opportunity of developing data science projects in a variety of domains, including:

• Data science for society and policy
• Data science for economics and finance
• Data science for culture and the humanities
• Data science for industry and manufacturing
• Data science for biology and health
• Data science for the hard and environmental sciences
• Data science ethics and legal aspects
• Data science techniques and methods

The PhD leverages the critical mass of data science labs and researchers accumulated in Pisa since early 2000’s, across the University of Pisa, the ISTI and IIT institutes of the CNR (National Research Council), Scuola Normale Superiore, Sant’Anna School of Advanced Studies and the IMT School for Advanced Studies Lucca. These labs gave rise to pioneering European projects in big data analytics and data science, as well as to the earliest educational programs for data scientists at graduate and PhD level. In 2015, the European Commission has chosen this hub as the coordinator of the European Research Infrastructure for Big Data Analytics & Social Mining,  SoBigData     http://www.sobigdata.eu .  This initiative provides an ecosystem of data, analytics and competences to support inter-disciplinary open data science and data-driven innovation, within an ethical framework of transparency, privacy, and responsibility. SoBigData provides a unique platform for doctoral education in Data Science, recognized by the Ministry of Education, University and Research   [1] , where PhD students can carry out multi-disciplinary data-driven research.

The IMT School Representative for the Program is Prof. Rocco De Nicola .

   [1]  Rapporto MIUR BigData,    http://www.istruzione.it/allegati/2016/bigdata.pdf   pag. 33

For further information, please visit the   Program's website .

Teaching Activity

Teaching is articulated in two lines: alignment of data science skills, to create a common ground for students with diverse background, and applications of data science in disciplinary and multi-disciplinary contexts. For alignment, PhD students will have the opportunity to take selected courses offered by the post-graduate Master in “Big Data Analytics and Social Mining” (Master Big Data) of the University of Pisa, in collaboration with CNR, Scuola Normale Superiore, Sant’Anna School of Advanced Studies and SoBigData.eu. Available courses cover the basics of Data Science and Big Data Analytics:

• Big Data Sensing & Procurement (Analytical Web Crawling, Scraping, Web Search and Information Retrieval, Semantic Text Annotation, Big Data Sources, Crowdsensing)
• Big Data Mining (Data Mining, Machine Learning and Statistical Learning, Network Science and Social Network Analysis, Mobility Data Analysis, Web Mining, Nowcasting, Sentiment Analysis and Opinion Mining)
• Big Data Storytelling (Visualization, Visual analytics, Data Journalism)
• Big Data Ethics (Privacy-by-design, Data Protection Regulations, Responsible Data Science, Legal aspects of Data Science)
• Big Data Technologies (Data Management for Business Intelligence, High Performance & Scalable Analytics, NO-SQL Big Data Platforms).

A wide variety of PhD courses focusing on the multi-disciplinary applications of data science are offered by the participating institutions, also in synergy with existing disciplinary PhD programs. Students also have the opportunity to participate in summer schools organized in collaboration with international research institutions, and to the PhD+ program of the University of Pisa, for the development of entrepreneurial and innovation skills.

The PhD Board

Dino Pedreschi   (PhD Program Coordinator), University of Pisa Albert-Laszlo Barabasi , Northeastern University, Boston, USA Vincenzo Barone , Scuola Normale Superiore Roberta Bracciale , University of Pisa Chiara Cappelli , Scuola Normale Superiore Alessandro Cellerino , Scuola Normale Superiore Francesca Chiaromonte , Sant’Anna school of Advanced Studies Giulio Cimini , IMT School for Advanced Studies Lucca Marco Conti , National Research Council (CNR) Tommaso Cucinotta , Sant’Anna school of Advanced Studies Giuseppe De Pietro , National Research Council (CNR) Fabio Gadducci , University of Pisa Diego Garlaschelli , IMT School for Advanced Studies Fosca Giannotti , National Research Council (CNR) János Kertész , Central European University, Budapest Fabrizio Lillo , Università di Bologna Pietro Luigi Lopalco , University of Pisa Francesco Marcelloni , University of Pisa Stan Matwin , Dalhousie University, Halifax, CDN Anna Monreale , University of Pisa Elena Pavan , Scuola Normale Superiore Alex “Sandy” Pentland , MIT, USA Raffaele Perego , National Research Council (CNR) Andrea Piccaluga , Sant’Anna school of Advanced Studies Nadia Pisanti , University of Pisa Monica Pratesi , University of Pisa Chiara Maria Angela Roda , University of Pisa Salvatore Ruggieri , University of Pisa Tiziano Squartini , IMT School for Advanced Studies Lucca Franco Turini , University of Pisa

Call for applications

Details on upcoming and past calls for applications are available on the Program's website .

Department of Economics, Management and Statistics  DEMS

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PhD in Economics, Statistics and Data Science

• PhD in Strategic Innovation for Sustainable and Smart Ecosystems (SIS2E)
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The four-year PhD in Economics, Statistics and Data Science (ECOSTATDATA)  provides the most effective response to the important challenges which nowadays doctoral programmes in the areas of economics, statistics and data analytics, both in Italy and Europe, have to cope with: i) high qualification of the faculty, in terms of teaching abilities and publication records; ii) capability of attracting high quality students; iii) interdisciplinarity; iv) internationalization; v) relations with the non-academic job market; vi) placement of students who have successfully discussed their dissertations.

ECOSTATDATA builds upon the fruitful collaboration among economists, statisticians and data scientists from the Department of Economics, Management and Statistics (DEMS) and the Department of Statistics and Quantitative Methods of the University of Milano-Bicocca (UniMiB), which has started twenty years ago within the BSc in Statistics and Economics, as well as the MSc in Statistics and Economics and is going on with the more recent MSc in Data Science.

Coordinator : Prof.  Matteo Manera

Deputy Coordinator : Prof.  Giorgio Vittadini

NEW!!! Call for applications 2024-2025 (XL cycle)

A.A. 2024-2025 (cycle XL)

Call for Applications

DEMS - University of Milano-Bicocca, Italy

The Department of Economics, Management and Statistics (DEMS) of the University of Milano-Bicocca invites applications to its PhD Programme in Economics, Statistics and Data Science (ECOSTATDATA) for the academic year 2024-25 (XL cycle).

The PhD Programme is articulated in  three curricula , Economics (ECO), Statistics (STAT) and Big Data & Analytics for Business (BIDAB). The length of the PhD Programme is  four years , starting in late October 2024 (the precise starting date will be announced in due course on the PhD website).

The Call of Applications 2024-2025 offers at least 10 fully-funded scholarships .

The selection procedure is regulated by the official Call for Applications (Bando di Concorso), which will be published in the Doctoral School’s and in the PhD programme websites  on April 12, 2024 , with deadline on May 14, 2024.

The official Call for Applications contains detailed information on: i) the documents which each candidate has to submit; ii) structure, contents and timing (May 27, 2024 - June 21, 2024) of the entrance examination; iii) description of the projects related to the scholarships and positions offered.

The official Call for Applications will be published  here .

The PhD programme (in a nutshell...)

Introduction.

ECOSTATDATA belongs to the PhD School of UniMiB, it is affiliated to DEMS, it lasts four years and it is articulated in three curricula, the original two curricula Economics (ECO) and Statistics (STAT), and, starting from cycle XXXVII (academic year 2021-2022), the “new” curriculum Big Data & Analytics for Business (BiDAB) .

The first-year teaching activities are mainly devoted to structured courses (tool courses), which are compulsory. Some of these courses are fixed and specific to each curriculum, some are in common between the three curricula, some other courses are chosen by students within each curriculum.

The second-year teaching activities take the form of less structured courses (elective courses or reading groups).

In general, the first-year courses are offered by “internal” teachers, while second-year courses are often open to the collaboration of foreign instructors (visiting scholars).

The curriculum Economics (ECO)

This curriculum is indicated to students with a strong background in quantitative economics and provides advanced training in econometrics, microeconometrics, time series analysis, microeconomics and macroeconomics.

The curriculum Statistics (STAT)

This curriculum is designed for students with a strong background in statistics, both methodological and applied , and provides advanced training in probability, stochastic processes, statistical inference, Bayesian statistics, statistical learning, statistical modelling, computational statistics and data analysis.

The “new” curriculum Big Data & Analytics for Business (BiDAB)

This curriculum starts from cycle XXXVII (academic year 2021-2022) , and provides students with rigorous training in data management and programming, with focus on: the analysis of large amounts of structured and unstructured data (natural language); the main paradigms of big data and data visualization, based on the use of innovative techniques of machine learning, text and web mining.

“Flexible” and “training” profiles

By means of appropriate sequences of courses, suggested and monitored by the Programme Committee and the supervisors, students are able to build up “flexible” profiles, which are mainly addressed to scientific research, both in universities or in non-academic institutions, at national or international level.

ECOSTATDATA facilitates the interaction between economic, statistical and data management skills by proposing innovative “training” profiles, which are  mainly addressed to the non-academic job market. The “training” profiles aim at:

• offering to the non-academic job market high-level skills which are not currently available;
• attracting students who are interested in ECOSTATDATA as a way to gain new and advanced skills to be immediately spent into that segment of the job market which is not academically- or research-oriented;
• eliciting the collaboration of high-quality national multi-national companies, which are active in human capital investment and are ready to use the instruments of the executive doctorate, the apprenticeship contracts as well as the direct financing of a PhD scholarship on specific research projects.

Length of the programme

The current length of many PhD programmes in economics, statistics and data science in Italy, including the PhD in Economics DEFAP-Bicocca and in Statistics and Mathematical Finance of UniMiB, is three years. This length is insufficient to guarantee that the PhD theses meet the quality standards achieved by the best European PhD programmes. For this reason, ECOSTATDATA lasts four years . This duration is in line with the recent choices of some of the best Italian PhD programmes in economics, statistics and data science, as well as the PhD programmes in this area offered by the most prestigious European academic institutions.

Interdisciplinarity

ECOSTATDATA fosters interdisciplinary research activities, by favouring co-tutorships between economists, statisticians and data scientists, as well as through the “flexible” and “training” profiles.

Relations with the non-academic job market

ECOSTATDATA is particularly active in collaborating with national, multi-national, high-quality and innovation-oriented companies. In particular, ECOSTATDATA is able to: i) offer high-level skills which are not currently available on the non-academic job market; ii) attract students who are interested in ECOSTATDATA as a way to gain new and advanced skills to be immediately spent into that segment of the job market which is not academically- or research-oriented; iii) elicit the collaboration of high-quality national multi-national companies, which are active in human capital investment and are ready to use the modern instruments of the executive doctorate, the apprenticeship contracts as well as the direct financing of PhD scholarships on specific research projects.

Internationalization

The international experience which has flourished within the PhD in Economics DEFAP-Bicocca and the PhD in Statistics and Mathematical Finance of UniMiB, together with the professional networks developed by many faculty members, guarantees that ECOSTATDATA is particularly active in collaborating with prestigious foreign universities, in terms of both students and faculty members exchange programs and joint degrees.

ECOSTATDATA is managed by two bodies:

• the Programme Committee (PC), that is the executive and decision-making board composed by full professors, associate professors and researcher of UniMiB and from other renowned Italian and foreign universities and research institutions;
• the Advisory Board (AB), which collaborates with the PC to organize the teaching and research activities of the programme, is headed by the programme Coordinator and is formed by a limited number of professors and researchers who are representative of the three curricula.

Teaching activities

The teaching activities proposed by ECOSTATDATA are organized during the first two years and differ for each curriculum, although some courses are common. Some economics courses at the first and the second year within the curriculum Economics can be offered jointly with the PhD programme in Economics and Finance of the Catholic University of Milano.

First- year courses

• Curriculum Economics (selected courses)

Mathematics; Computational Statistics I; Econometrics; Microeconometrics; Time Series Analysis; Microeconomics; Macroeconomics; Research Methods; Finance.

• Curriculum Statistics (selected courses)

Mathematical Analysis, Numerical Optimization, Probability, Stochastic Processes, Bayesian Statistics, Statistical Inference, Statistical Learning, Computational statistics II, Statistical Modelling, R for Data Science, Data Management.

• Curriculum Big Data & Analytics for Business (selected courses)

Databases for Structured/Unstructured Data (SQL); Programming in Python; Data Quality and Cleaning for Big Data; Architecture for Big Data Processing; Machine Learning; Cloud & Distributed Algorithm; Data Mining; Natural Language processing and Understanding; Human-Centered AI; Social Media Analysis; Semantic Web; Deep Learning and Computer Vision for Business; Data Visualization & Visual Analysis.

Second-year courses

Second-year courses are mainly “reading groups”, that are built upon the research interests of both instructors and students, and are  articulated into one/two introductory lecture/s and a series of meetings where students critically discuss the readings assigned by the instructor during the initial lecture.

The second-year courses are generally offered during the first part of the second year, in order forstudents to be full-time dedicated to their dissertations as early as possible.

Within each curriculum, a careful selection of courses, monitored by the PC and the student’s supervisor, allows each student to identify a “flexible” profile, which coherent with his/her research interests.

Generally, structured courses have written exams, while the exams associated with the reading groups are more flexible (e.g. written projects and/or oral presentations). The organization of the exams (i.e. form, number of questions, etc.) is decided by the PC and communicated to students at the beginning of each course. 

Monitoring the quality of teaching

The PC runs every year a systematic evaluation of the quality of the courses offered by the PhD programme, by submitting to each student of a given course a detailed questionnaire. Data from the questionnaires are elaborated statistically, sent to each instructor, and discussed within the PC, in order to identify potential problems and solutions.

Admissions to the second year and to years after the second

Admission to the second year is based on the performance of each student in the first-year exams, including the number of “fail” and the number of “resits” each student has been given. Admissions to the third and the fourth years are based on the progresses of the research work. Rules on admission to the second and subsequent years, as well as all the other rules regulating the teaching and research activities of ECOSTAT are formalized by the PC and communicated to each student after enrollment.

Research activities

The Programme Committee (PC) approves the (minimum) number of papers which form a typical PhD dissertation, namely 2. These papers have to be self-contained, independent and potentially publishable on high-quality internationally refereed journals.

Supervision

In order to facilitate students in identifying a sound research project and a suitable supervisor, within the first part of the year the PC organizes a presentation of the research groups which are active among the PC and the Advisory Board (AB) members. Supervisors are asked to systematically monitor the progresses made by their supervisees and periodically report to the PC about the proceedings of their dissertations.

PhD students, especially from the second year, are strongly invited to attend the department seminars organized on a weekly basis at UniMiB. Students of both curricula are also invited to present the progress of their research work in specific seminars, which are part of the student’s evaluation process and, if possible, are jointly organized in order to enhance cross-fertilization between economists, statisticians and data scientists. 

Admission to third and fourth year

Admission to the third and fourth year is formalized by the PC, based on the evaluation of the student’s research work. Admission to the third year takes also into account the performance of each student in the second-year exams.

Admission to external evaluation

Fourth-year students should present, by the end of the year, the final version of their dissertation in front of the PC. If possible, each presentation will be assigned a discussant. The admission to the external reviewers is formalized by the PC, based on the overall evaluation of the PhD thesis.

Thesis discussion

Based on the reports of the external reviewers, students are admitted to the discussion in front of the Evaluation Committee either with minor or major revisions. Students who have successfully defended their dissertation are awarded by the Evaluation Committee the title of “PhD in Economics and Statistics” (students enrolled in cycles XXXIV, XXXV and XXXVI) or the title of “PhD in Economics, Statistics and Data Science” (students enrolled from cycle XXXVII). Students can request to (and obtain from) the Administrative Offices of UniMiB an official document reporting the specific curriculum they have been enrolled in.

ECOSTATDATA takes care of the optimal placement of its students. On this respect, the Programme Committee is very active in: i) providing students with systematic and detailed information on the job market, domestic and international, academic and non-academic; ii) advising and assisting students who intend to apply for academic positions abroad.

Programme committee

Research groups.

The research activities which characterize the PhD programme in Economics, Statistics and Data Science (ECOSTATDATA) are carried out by an active and lively community of junior and senior researchers.

Within DEMS, researchers are organized in clusters , among which the most relevant for ECOSTATDATA are:

- Business, economic and social statistics (coordinator: Prof. Pelagatti)

- Empirical microeconomics and microeconometrics (coordinator: Prof. Manera)

- Experimental and behavioural economics (coordinator: Prof. Stanca)

- Macroeconomics and macroeconometrics (coordinator: Prof. Morana)

- Microeconomics: theory and applications (coordinator: Prof. Gilli)

- Statistics (coordinator: Prof. Ongaro)

- Strategy, organization and innovation (coordinator: Prof. Torrisi)

Detailed information about people involved in each cluster can be found here .

The other two main groups of researchers supporting the programme are affiliated to the Department of Statistics and Quantitative Methods (DiSMeQ) of UniMiB and to the Department of Statistics (DiSTAT), Catholic University of Milano.

Detailed information about the research activities carried on by the DiSMeQ members can be found here .

Detailed information about the research activities carried on by the DiSTAT members can be found here .

Ex-alumni - XXXIV cycle

Supervisor(s): Prof. Silvia Biffignandi , University of Bergamo

Ex-alumni - XXXV cycle

Supervisor(s): Prof. Francesca Greselin , University of Milano-Bicocca; Prof. Ricardas Zitikis , University of Western Ontario, CA

Milano PhD Workshop 2024

The ECOSTATDATA PhD students are happy to announce the second edition of the Milano PhD Workshop , that will be held at the premises of the University of Milano-Bicocca, September 23-27, 2024.

The event is jointly organized with the PhD students in economics of the major universities in the Milanese area.

The program of the event is under construction and will be available shortly.

For details you can contact the local organizers:

PhD students' seminar series 2023-2024

We are very happy to announce this new initiative: the  ECOSTATDATA PhD Seminar Series!

This initiative aims to create a friendly environment where all PhD students at DEMS have the opportunity to present their own research or research proposal to obtain constructive feedback from peers and senior researchers.

Regular reminders before each presentation will be sent, and we really hope you will join this initiative. Your presence and support will be key to make this a success!

The Organizers 

@Angelica Bertucci  

@Ludovica De Carolis  

@Matteo Ferraro  

@Gregorio Ghetti  

@Lorena Popescu  

March 28, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker: Andrea Sorrentino

April 18, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Francesco Ferlaino

Field: Macroeconomics

May 09, 2024 - Aula Seminari (U7 - 2104) 17:00

Speaker:  Luca Danese

Field: Bayesian Nonparametrics

May 16, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Angelica Bertucci

May 23, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Matteo Ferraro

May 30, 2024 Aula Seminari (U7 - 2104) 12:00

Speaker:  Lucia Tommasiello

June 6, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Mattia Longhi

June 13, 2024 - Aula Seminari (U7 - 2104) 17:00

Speaker:  Claudia Sartirana

June 20, 2024 - Aula Seminari (U7 - 2104) 17:00

Speaker 1:  Ludovica De Carolis

Speaker 2:  Jiefeng Bi

Field: Bayesian Statistics

Past events (selected) 2018-2024

The PhD in Economics, Statistics and Data Science (ECOSTATDATA), the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca, joint with the Italian Society of Econometrics (SIdE), the Free University of Bolzano, the Fondazione Eni Enrico Mattei (FEEM), the International Association of Applied Econometrics (IAAE) and the Rimini Center for Economic Analysis (RCEA), have organized the 4th Italian Workshop on Econometrics and Empirical Economics (IWEEE 2024) - Climate and Energy Econometrics , at the Free University of Bolzano, during the period January 25-26, 2024. 

The PhD in Economics, Statistics and Data Science (ECOSTATDATA), the Center for European Studies (CefES) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Bayesian Structural VAR, held by Prof. Fabio Canova , BI Norwegian Business School, during the period November 9-14, 2023. 

The PhD in Economics, Statistics and Data Science (ECOSTATDATA) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Statistical Learning, held by  Prof. Botond Szabo , Bocconi University, during the period October 5-27, 2021.

The PhD in Economics, Statistics and Data Science (ECOSTATDATA) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Statistical Learning, held by Prof. Omiros Papaspiliopoulos , Bocconi University , during the period October 5-27, 2021. Detailed information on this course (instructor, objectives, programme, references, prerequistes) can be found  here

The PhD in Economics, Statistics and Data Science (ECOSTATDATA), the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca and the Fondazione Eni Enrico Mattei (FEEM), Milano, have organized the summer school on Frontiers of Energy Econometrics , at the Como Lake School of Advanced Studies, during the period September 13-17, 2021. Detailed information on the programme and the application procedure can be found on the summer school website:  https://toee.lakecomoschool.org/

The PhD in Economics and Statistics (ECOSTAT) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Statistical Learning, held by Prof. Rajen Shah , University of Cambridge, during the period October 5-30, 2020. Detailed information on this course (instructor, objectives, programme, references, prerequistes) can be found  here.

The PhD in Economics and Statistics (ECOSTAT) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized and hosted the course Statistical Learning and Big Data, held by Prof.  Sharon Rosset , Tel Aviv University, during the period October 7-18 2019. Detailed information on this course (instructor, objectives, programme, references, prerequistes) can be found  here .

The PhD programme in Economics and Statistics (ECOSTAT) has sponsored the 1 st  CefES International Conference on European Studies, to be held at the University of Milano-Bicocca, Building U6, on June 10th-11 th  2019. Details on this event can be found  here .

The PhD programme in Economics and Statistics (ECOSTAT) has sponsored the International Conference on Econometric Models of Climate Change, held at the University of Milano-Bicocca on August 29th-30 th  2019. Details on this event can be found  here .

Within the Seminar Series DEMS-ECOSTAT,  Prof. Peter M Robinson  (LSE),  has presented the paper titled “Long-range dependent curve time series” (joint with Degui Li and Han Lin Shang). Prof. Robinson is one of the most famous econometricians worldwide and has been in the editorial boards of the most influential journals in econometrics and statistics, from Econometrica to the Journal of Econometrics, from the Journal of the American Statistical Association to the Annals of Statistics. Peter Robinson’s presentation is available  here , while his paper is available  here . This  event  has been held on February 14th 2019, 12.00am, at the Aula del Consiglio, U7, fourth floor, Piazza dell’Ateneo Nuovo 1, 20126 - Milano.

Within the celebrative events of the Twentieth Anniversary of the University of Milano-Bicocca, the Department of Economics, Management and Statistics, in collaboration with the School for Graduate Studies, has organized the International Conference on  The Mathematics of Subjective Probability .  This event was held on September  3rd-5th  2018, at Room U4/2, Piazza della Scienza 1, 20126 - Milano.

Within the celebrative events of its Twentieth Anniversary, the University of Milano-Bicocca, in collaboration with its School for Graduate Studies, has organized the  Lectio Magistralis of Prof. Robert Engle  (NYU University), winner of the 2003 Nobel Memorial Prize in Economic Sciences, on “A Financial Approach to Environmental Risk”. This event was held on June 22nd 2018, 10.00am, at the Auditorium Guido Martinotti U12, Via Vizzola 5, 20126 - Milano.

The Center for European Studies (CefES-DEMS-UNIMIB), the PhD program in Economics and Statistics (ECOSTAT-UNIMIB), and the  Department of Economics, Management and Statistics  (DEMS-UNIMIB) have organized the one-day international conference on  Economic and Financial Implications of Climatic Change .  Two plenary sessions  on the economic and financial implications of climatic change have been organized on June 22 nd  2018, following Prof. Robert Engle’s talk, from 11.30am to 4.45pm, at the Auditorium Guido Martinotti U12, Via Vizzola 5, 20126 - Milano.

XXXVII cycle - Teaching activities - Year II (terms I - II) - reading groups

Reading groups (rg) offered in academic year 2022-23 (xxxvii cycle – ii year) for the curriculum in economics (eco):.

I term (October 2022 – December 2022)

-  Social Network Theory  (Instructor: Prof. F. Panebianco, Catholic University of Milano)

-  Applications of Game Theory  (Instructor: Prof. M. Gilli, University of Milano-Bicocca)

-  Empirical Banking  (Instructor: Prof. Elena Beccalli, Catholic University of Milano)

-  Advanced Asset Pricing and Portfolio Management  (Instructor: Prof. A. Tarelli, Catholic University of Milano)

-  Empirical Corporate Finance  (Instructor: Prof. E. Croci, Catholic University of Milano)

-  Programming in Python  (Instructor: Prof. L. Viarengo, Catholic University of Milano)

II term (January 2023 – April 2023)

-  Spatial Models  (Instructor: Prof. S. Colombo, Catholic University of Milano)

-  Financial Frictions  (Instructor: Prof. D. Delli Gatti, Catholic University of Milano)

-  The Microeconomics of International Trade  (Instructor: Prof. V. Gattai, University of Milano-Bicocca)

-  Innovation and Industrial Evolution  (Instructor: Prof. C. Garavaglia, University of Milano-Bicocca)

-  Structural VAR Models  (Instructors: Proff. V. Colombo, G. Rivolta, Catholic University of Milano)

-  Applied Health Economics and Policy  (Instructors: Proff. G. Turati, E. Cottini, L. Salmasi, Catholic University of Milano)

Note:  the RG for the curriculum ECO are offered jointly with the PhD in Economics and Finance of the Catholic University of Milano (CUM). CUM is in charge of the timetable of each RG, whose updated version can be found  here . 

The following extra-RG are offered by ECOSTATDATA in the II term:

-  Expected Utility and Decision Theory  (Instructor: Prof. G. Cassese, University of Milano-Bicocca)

-  Estimated DSGE Models  (Instructor: Prof. Alice Albonico, University of Milano-Bicocca)

-  Authority and Delegation  (Instructor: Prof. Irene Valsecchi, University of Milano-Bicocca)

Note:  the timetable of the extra-RG is available  here . 

Reading Groups (RG) offered in academic year 2022-23 (XXXVII cycle – II year) for the curriculum in Statistics (STAT):

I term (October 2022 – December 2022)

-  The Dependent Dirichlet Process and Related Models  (Instructors: Proff. F. Camerlenghi, B. Nipoti, University of Milano-Bicocca)

-  Some Issues in Statistical Modelling  (Instructor: Prof. R. Borgoni, University of Milano-Bicocca)

-  Empirical Bayes in Bayesian Inference  (instructor: Prof. S. Rizzelli, Catholic University of Milano)

-  Automated Machine Learning & Neural Architectural Search  (Instructor: Prof. A. Candelieri, University of Milano-Bicocca)

-  Deep Learning  (Instructor: Prof. M. Borrotti, University of Milano-Bicocca)

Note:  the timetable of the RG for the curriculum STAT is available  here . 

Reading Groups (RG) offered in academic year 2022-23 (XXXVII cycle – II year) for the curriculum Big Data & Analytics for Business (BIDAB):

II term (January 2023 – April 2023)

-  Databases for Structured and Unstructured Data – SQL  (POSTPONED) (Instructor: Prof. F. Mercorio, University of Milano-Bicocca)

-  Human-centered AI  (Instructor: Prof. F.M. Zanzotto, University of Roma-Tor Vergata)

Note:  the timetable of the RG for the curriculum BIDAB is available  here . 

XXXVIII cycle - Teaching activities - Year I (terms I - II - III - IV) - courses

The I term teaching activities start on 24 October 2022 and end on 23 December 2022. The I term exam session starts on 9 January 2023 and ends on 13 January 2023.

Note:  the timetable of the I term courses is available  here

The courses/modules offered during the I term for the curriculum Economics (ECO) are:

-  Computational Statistics I  (Instructor: Prof. G. Bertarelli, University of Pisa)

-  Mathematics – Linear algebra  (Instructor: Prof. N. Pecora, Catholic University of Milano)

-  Mathematics I  (Instructor: Prof. D. Visetti, University of Milano-Bicocca);

-  Mathematics II  (Instructor: Prof. F. Cavalli, University of Milano-Bicocca);

-  Mathematics III  (Instructor: Prof. M. Longo, Catholic University of Milano)

The courses/modules offered during term I for the curriculum Statistics (STAT) are:

-  Mathematical Analysis  (Instructors: Prof. C. Zanco, University of Milano; Proff. C.A. De Bernardi, E. Miglierina, Catholic University of Milano)

-  Numerical Optimization  (Instructor: Prof. L. Mascotto, University of Milano-Bicocca) 

The courses/modules offered during term I for the curriculum Big Data & Analytics for Business (BiDAB) are:

-  Programming in Python  (Instructor: Prof. M. Cesarini, University of Milano-Bicocca)

-  Architecture for Big Data Processing  (Instructor: Prof. V. Moscato, University of Napoli)

-  Architecture for Big Data Processing Lab  (Instructor: Prof. G. Sperlì, University of Napoli)

The II term teaching activities start on 16 January 2023 and end on 5 April 2023. The II term exam session starts on 17 April 2023 and ends on 21 April 2023. 

The courses/modules offered during the II term for the curriculum Economics (ECO) are:

-  Econometrics I  (Instructor: Prof. M. Manera, University of Milano-Bicocca)

-  Econometrics I – Tutorials  (Instructor: Dr. C. Cattaneo, European Institute on Economics and the Environment)

-  Econometrics II  (Instructor: Prof. M.L. Mancusi, Catholic University of Milano)

-  Econometrics II – Tutorials  (Instructor: Dr. E. Villar, Catholic University of Milano)

-  Econometrics III  (Instructor: Prof. A. Ugolini, University of Milano-Bicocca)

-  Econometrics III - Tutorials  (Instructor: Dr. D. Valenti, Fondazione Eni Enrico Mattei)

-  Microeconomics I  (Instructor: Prof. M. Mantovani, University of Milano-Bicocca)

-  Microeconomics I – Tutorials  (Instructor: Dr. F. Campo, University of Milano-Bicocca)

-  Microeconomics II  (Instructtor: Prof. M. Gilli, University of Milano-Bicocca)

-  Microeconomics II – Tutorials  (Instructor: Prof. M. Gilli, University of Milano-Bicocca)

-  Microeconomics III  (Instructor: Prof. L. Colombo, Catholic University of Milano)

-  Microeconomics III – Tutorials  (Instructor: Dr. D. Bosco, University of Milano-Bicocca)

-  Microeconomics IV  (Instructor: Prof. P. Bertoletti, University of Milano-Bicocca)

-  Microeconomics IV – Tutorials  (Instructor: Dr. G. Crea, University of Pavia)

Note:  the timetable of the II term courses for the curriculum ECO is available  here .

The courses/modules offered during the II term for the curriculum Statistics (STAT) are:

-  Probability I & II  (Instructor: Prof. F. Camerlenghi, University of Milano-Bicocca)

-  Stochastic Processes  (Instructor: Prof. B. Buonaguidi, Catholic University of Milano)

-  R for Data Science  (Instructor: Prof. A. Gilardi, University of Milano-Bicocca)

-  Statistical Inference I  (Instructor: Prof. A. Caponera, University of Milano-Bicocca)

Note:  the timetable of the II term courses for the curriculum STAT is available  here .

The courses/modules offered during the II term for the curriculum Big Data & Analytics for Business (BIDAB) are:

-  Probability  (Instructor: Prof. A. Di Brisco, University of Piemonte Orientale)

-  Statistical Inference I  (Instructor: Prof. R. Ascari, University of Milano-Bicocca)

Note:  the timetable of the II term courses for the curriculum BIDAB is available  here .

The III term teaching activities start on 26 April 2023 and end on 7 July 2023. The III term exam session starts on 17 July 2023 and ends on 21 July 2023. 

The courses/modules offered during the III term for the curriculum Economics (ECO) are:

- Macroeconomics I (Instructor: Prof. G. Femminis, Catholic University of Milano)

- Macroeconomics II (Instructor: Prof. A. Albonico, University of Milano-Bicocca)

- Macroeconomics III (Instructor: Prof. R. Masolo, Catholic University of Milano)

- Macroeconomics IV (Instructor: Dr. B. Barbaro, University of Milano-Bicocca)

-  Computational Statistics II  (Instructor: Prof. A. Pini, Catholic University of Milano)

-  Research Methods  (Instructors: Prof. T. Colussi, Catholic University of Milano; Prof. K. Aktas, University of Milano-Bicocca)

- Finance I – Empirical Corporate Finance (Instructor: Prof. A. Signori, Catholic University of Milano)

- Finance II – Asset Pricing Theory (Instructor: Prof. A. Sbuelz, Catholic University of Milano)

- Finance III – Banking (Instructors: Proff. M. Migliavacca, F. Pampurini, Catholic University of Milano)

Note:  the timetable of the III term courses for the curriculum ECO is available here .

The courses/modules offered during the III term for the curriculum Statistics (STAT) are:

-  Statistical Inference II  (Instructor: Prof. A. Solari, University of Milano-Bicocca)

- Bayesian Statistics (Instructors: Prof. R. Argiento, University of Bergamo; Proff. B. Nipoti, T. Rigon, University of Milano-Bicocca)

- Data Management (CANCELLED)

Note:  the timetable of the III term courses for the curriculum STAT is available here .

The courses/modules offered during the III term for the curriculum Big Data & Analytics for Business (BIDAB) are:

- Technology and Innovation Management (Instructors: Proff. S. Torrisi, L. D'Agostino, F. Di Pietro, M. Guerzoni, University of Milano-Bicocca)

- Machine Learning (Instructor: Prof. L. Malandri, University of Milano-Bicocca)

- Natural Language Understanding (CANCELLED)

-  Social Media Analytics  (Instructor: Prof. R. Boselli, University of Milano-Bicocca)

Note:  the timetable of the III term courses for the curriculum BIDAB is available here .

The IV term teaching activities start on 4 September 2023 and end on 20 October 2023. The IV term exam session starts on 23 October 2023 and ends on 27 October 2023. 

Note:  the timetable of the IV term courses is under construction and is currently shared with all the ECOSTATDATA students, who can monitor online any updates/modifications.

The courses/modules offered during the IV term for the curriculum Statistics (STAT) are:

- Statistical Learning (POSTPONED)

- Statistical Modelling I (Instructor: Prof. F. Castelletti, Catholic University of Milano)

- Statistical Modelling II (Instructor: Prof. F. Greselin, University of Milano-Bicocca)

- Statistical Modelling III (Instructor: Dr. S. Verzillo, European Commission - Joint Research Center)

- Statistical Modelling IV (Instructors: Prof. F. Pennoni, University of Milano-Bicocca; Prof. F. Bartolucci, University of Perugia)

The courses/modules offered during the IV term for the curriculum Big Data & Analytics for Business (BIDAB) are:

- Statistical Inference II (Instructor: Prof. R. Ascari, University of Milano-Bicocca)

- Explainable AI for Business Value (Instructor: Prof. F. Mercorio, University of Milano-Bicocca)

- Deep Learning and Computer Vision for Business (Instructor: Prof. E. Frontoni, Polytechnic University of Marche, TBC)

XXXVIII cycle - Teaching activities - Year II (terms I - II) - reading groups

Reading groups (rgs) offered in academic year 2023-24 (xxxviii cycle – ii year) for the curriculum economics (eco):.

I term (October 2023 – December 2023) and II term (January 2024 – April 2024)

Note:  the RGs for the curriculum ECO are offered jointly with the PhD in Economics and Finance of the Catholic University of Milano. Detailed information on each RG and its timetable can be found  here . 

Reading Groups (RGs) offered in academic year 2023-24 (XXXVIII cycle – II year) for the curriculum Statistics (STAT):

I term (November 2023 – December 2023) and II term (January 2024 – April 2024)

Note:  the timetable of the RGs for the curriculum STAT is shared online (via Google Calendar) with students officially enrolled in the PhD program. 

- RG Approximate Bayesian Computational Methods (Instructor: Dr. A. Fasano , Catholic University of Milano)

- RG Automated Machine Learning & Neural Architectural Search (Instructor: Prof. A. Candelieri , University of Milano-Bicocca)

- RG Spatio-temporal Data (Instructors: Prof. R. Borgoni  and Dr. P. Maranzano , University of Milano-Bicocca)

- RG Some Issues on Statistical Modelling (Instructor: Prof. R. Borgoni , University of Milano-Bicocca)

- RG Deep Learning (Instructor: Prof. M. Borrotti , University of Milano-Bicocca)

Reading Groups (RGs) offered in academic year 2023-24 (XXXVIII cycle – II year) for the curriculum Big Data & Analytics for Business (BIDAB):

I term (November 2023 - December 2023) and   II term (January 2024 – April 2024)

Note:  the timetable of the RGs for the curriculum BIDAB is shared online (via Google Calendar) with students officially enrolled in the PhD program.

- RG Natural Language Processing (Instructor: Dr. A. Seveso , University of Milano-Bicocca)

- RG Generative AI (Instructor: Dr. Navid Nobani , University of Milano-Bicocca)

XXXIX cycle - Teaching activities - Year I (terms I - II - III) - courses

The I term teaching activities start on 23 October 2023 and end on 22 December 2023. The I term exam session starts on 8 January 2024 and ends on 12 January 2024.

Note:  the timetable of the I term courses is shared online (via Google Calendar) with all students officially enrolled in the PhD program.

-  Mathematics – Linear algebra  (Instructor: Dr. N. Pecora , Catholic University of Milano)

-  Mathematics I  (Instructor: Dr. D. Visetti , University of Milano-Bicocca);

-  Mathematics II  (Instructor: Prof. F. Cavalli , University of Milano-Bicocca);

-  Mathematics III  (Instructor: Prof. M. Longo , Catholic University of Milano)

- Microeconomics I (Instructor: Prof. M. Mantovani , University of Milano-Bicocca)

-  Mathematical Analysis I-II-III  (Instructors: Dr. J. Somaglia , Polytechnic of Milano; Proff. C.A. De Bernardi, E. Miglierina, Catholic University of Milano)

-  Numerical Optimization  (Instructor: Dr. L. Mascotto , University of Milano-Bicocca) 

-  Programming in Python  (Instructor: Dr. M. Cesarini , University of Milano-Bicocca)

-  Architecture for Big Data Processing & Lab  (Instructors: Prof. V. Moscato and Dr. G. Sperlì , University of Napoli)

The II term teaching activities start on 15 January 2024 and end on 27 March 2024. The II term exam session starts on 8 April 2024 and ends on 12 April 2024.

Note:  the timetable of the II term courses is shared online (via Google Calendar) with all students officially enrolled in the PhD program.

- Microeconomics II (Instructor: Prof. M. Gilli , University of Milano-Bicocca)

- Microeconomics III (Instructors: Prof. L. Colombo and Dr. M. Magnani , Catholic University of Milano)

- Microeconomics IV (Instructors: Prof. P. Bertoletti , University of Milano-Bicocca, and Dr. G. Crea , University of Pavia)

- Econometrics I (Instructors: Prof. M. Manera , University of Milano-Bicocca, and Dr. C. Cattaneo , European Institute on Economics and the Environment)

- Econometrics II (Instructors: Dr. A. Ugolini , University of Milano-Bicocca, and Dr. D. Valenti , Polytechnic of Milano)

- Econometrics III (Instructors: Prof. M.L. Mancusi and Dr. E. Villar , Catholic University of Milano)

- Computational Statistics I (Instructor: Dr. G. Bertarelli , University of Venezia)

The courses/modules offered during the II term for the curriculum Statistics (STAT) are:

- Probability I-II (Instructor: Prof. F. Camerlenghi , University of Milano-Bicocca)

- Stochastic Processes (Instructor: Dr. B. Buonaguidi , Catholic University of Milano)

- Statistical Inference I (Instructor: Dr. A. Caponera , Luiss Guido Carli University) 

- R for Data Science (Instructor: Dr. A. Gilardi , Polytechnic of Milano)

The courses/modules offered during the II term for the curriculum Big Data & Analytics for Business (BiDAB) are:

- Probability (Instructor: Prof. A. Di Brisco , University of Piemonte Orientale)

-  Statistical Inference I (Instructor: Dr. R. Ascari , University of Milano-Bicocca)

The III term teaching activities start on 15 April 2024 and end on 5 July 2024. The III term exam session starts on 15 July 2024 and ends on 19 July 2024.

- Computational Statistics II (Instructor: Prof. A. Pini , Catholic University of Milano)

- Macroeconomics I  (Instructor: Prof. G. Femminis , Catholic University of Milano)

- Macroeconomics II  (Instructors: Prof. A. Albonico , University of Milano-Bicocca)

- Macroeconomics III (Instructors: Dr. B. Barbaro , University of Milano-Bicocca and Prometeia)

- Macroeconomics IV (Instructors: Dr. R. Masolo , Catholic University of Milano)

- Research Methods (Instructors: Dr. S. Ghisolfi and Prof. T. Colussi , Catholic University of Milano)

The courses/modules offered during the III term for the curriculum Statistics (STAT) are:

- Statistical Inference II (Instructor: Prof. A. Solari , University of Venezia)

- Principles of Bayesian Statistics (Instructor: Prof. B. Nipoti , University of Milano-Bicocca)

- Bayesian Computations (Instructor: Dr. T. Rigon , University of Milano-Bicocca) 

- Bayesian Modelling (Instructor: Prof. R. Argiento , University of Bergamo)

The courses/modules offered during the III term for the curriculum Big Data & Analytics for Business (BiDAB) are:

-  Statistical Inference II (Instructor: Dr. R. Ascari , University of Milano-Bicocca)

- Social Media Analytics (Instructor: Dr. R. Boselli , University of Milano-Bicocca)

- Machine Learning (Instructor: Dr. L. Malandri , University of Milano-Bicocca)

- Technology and Innovation Management I (Instructor: Prof. S. Torrisi , University of Milano-Bicocca)

- Technology and Innovation Management II (Instructor: Prof. M. Corsino , University of Milano-Bicocca)

- Technology and Innovation Management III (Instructor: Dr. F. Di Pietro , University of Milano-Bicocca)

- Technology and Innovation Management IV (Instructor: Prof. M. Guerzoni , University of Milano-Bicocca)

The ADSAI PhD program

“Artificial Intelligence is the new electricity.” Andrew Ng, Co-founder and lead of Google Brain.

The Applied Data Science and Artificial Intelligence (ADSAI) PhD program at the Univeristy of Trieste started with the 2021 cohort , and since 2021 ADSAI is also part of the Italian National PhD program in Artificial Intelligence .

• Frequenty Asked Questions (FAQ) .
• Contact: [email protected]

Motivation and objectives

Why a PhD in applied Data Science and Artificial Intelligence?

The aim of the new ADSAI PhD programme is to train students to master the modern tools of Data Science and Artificial Intelligence, preparing them to create new analysis methods while giving them the expertise to apply cutting-edge tools to problems in the real world. Our research spans from theoretical to applied Machine Learning, by understanding how modern methodologies became popular we frame their current limitations and highlight their possibility of extension.

What do students achieve with the ADSAI Phd program?

ADSAI students will master modern concepts of machine learning and statistical analysis, corroborated by strong analytical problem-solving skills. For instance, graduates will be trained in deep learning and neural networks, in Bayesian methods and sampling algorithms for probabilistica graphical models, in probabilistic programming for scalable inference, high performance computing and software development.

Upon graduation, students will be proficient in delivering a complete Data Science solution to a complex real-world problem from beginning to end. Training in core disciplines will be complemented with the possibility to attend modules focusing on ethical aspects of data analysis, and the impact of technological development on regulations and society.

What is the marketplace of ADSAI students?

ADSAI graduates will be attractive for top-tier Data Science and innovation companies, and will have scientific profiles ready to enter the academic world, in Italy and abroad. Given the broad impact of Data Science and Artificial Intelligence in modern society, industry and business, graduates will have the opportunity for a fulfilling career in a variety of fields. Potential careers paths range from the financial sector to digital services, healthcare, technology companies, market research and many others.

Program structure and curricula

ADSAI is a 3-years PhD programme structured in two overlapping parts and three curricula:

[Part 1] in the first part (approx. 12 months), students spend most time training to consolidate theoretical and applied methodologies from the broad area of Data Science and Artificial Intelligence. This constitutes core knowledge-base to succesfully implement a PhD project in the second part of the programme, and serves to level differences across PhD students with different backgrounds;

[Part 2] in the second part (approx. 24 months)s, students undertake their PhD research work that culminates with a final PhD thesis at the end of the third year. Training in the last two years is intended to be more advanced and focused to the specific research area of the student.

Each student enrolls in one of three curricula, with the opportunity to pursue both pure and industry-related research questions:

• Industry and natural sciences , coordinated by Prof. Luca Bortolussi ;
• Life sciences and medicine , coordinated by Prof. Giulia Barbati ;
• Economy and society , coordinated by Prof. Domenico De Stefano .

Supervisors

The ADSAI faculty board is composed of a highly cross-disciplinary group of experts from the University of Trieste, which tightly interact with colleagues at the following institutions:

• International School for Advanced Studies (SISSA) ;
• International Centre for Theoretical Physics (ICTP) ;
• National Institute of Astrophysics (INAF) ;
• Area Science Park .

On occasions, external members include other researchers from the broader Trieste area.

What type of mentorship should a student expect?

Upon enrollment, ADSAI students identify at least one supervisor from the ADSAI faculty board . On occasion, some projects might involve the opportunity of a research placement in industry or in another research institute. In those cases, one or more external supervisors might also be available.

The supervisors will mentor the student to define the most effective training program during the whole ADSAI PhD program, taking into account the student’s background and objectives. The student will identify his/ her research question in tight collaboration with the supervisors. All students will be encouraged and supported to present their work at international conferences and meetings at the appropriate point in time of their studies.

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PhD in DATA SCIENCE

• DATA SCIENCE

Presentation

Università degli Studi di Roma "Tor Vergata" - Via Cracovia, 50, 00133 Roma RM

Data Analytics and Decision Sciences

• Application
• Data Science Colloquia

What is Data Science?

Data Science is emerging as a disruptive consequence of the digital revolution . Based on the combination of big data availability, sophisticated data analysis techniques, and scalable computing infrastructures , Data Science is rapidly changing the way we do business, socialize, conduct research, and govern society. It is also changing the way scientific research is performed. Model-driven approaches are supplemented with data-driven approaches. A new paradigm emerged, where theories and models and the bottom up discovery of knowledge from data mutually support each other. Experiments and analyses over massive datasets are functional not only to the validation of existing theories and models, but also to the data-driven discovery of patterns emerging from data, which can help scientists design better theories and models, yielding deeper understanding of the complexity of social, economic, biological, technological, cultural and natural phenomena.

Data science is an interdisciplinary and pervasive paradigm aiming to turn data into knowledge, born at the intersection of a diversity of scientific and technological fields: databases and data mining , machine learning and artificial intelligence , complex systems and network science , statistics and statistical physics , information retrieval and text mining , natural language understanding, applied mathematics . Spectacular advances are occurring in data-driven pattern discovery, in automated learning of predictive models and in the analysis of complex networks.

Within this context, the Ph.D. in Data Science is aimed at educating the new generation of researchers that combine their disciplinary competences with those of a “data scientist”, able to exploit data and models for advancing knowledge in their own disciplines, or across diverse disciplines. To this purpose, the Ph.D. in Data Science develops a mix of knowledge and skills on the methods and technologies for the management of large, heterogeneous and complex data, for data sensing (how to harvest data), for data analysis and mining (how to make sense of data), for data visualization and storytelling (how to narrate data), for understanding the ethical issues and the social impact of Data Science. The Ph.D. students will have the opportunity of developing data science projects in a variety of domains, including:

• Data science for society and policy
• Data science for economics and finance
• Data science for culture and the humanities
• Data science for industry and manufacturing
• Data science for biology and health
• Data science for the hard and environmental sciences
• Data science ethics and legal aspects
• Data science techniques and methods

Applications from graduate students from any discipline are welcome. The successful candidate is expected to possess a solid motivation and personal preparation, and a strong propensity towards quantitative studies in own field. More information on the Call for Application can be found on the dedicated pages of the Scuola Normale Superiore .

In Evidence

Academic year 2021-2022 (37th cycle).

As the Italian Ministry has decided to invest in a doctoral program on Artificial Intelligence, next year the Data Science Ph.D. will become one of the 5 nodes of a new national initiative: the National Artificial Intelligence Ph.D. All the partner institutions of the current program will join the new Ph.D.

The call for admissions to the National PhD in Artificial Intelligence is now open!

Interested in a multi-disciplinary PhD course oriented at cutting-edge research in human-centered Artificial Intelligence and its impacts on society? Apply to one of 44 fully-funded positions at the National PhD in AI – “Society” area:

https://dottorato.unipi.it/index.php/en/application-process-for-the-acad...

Deadline: July 23, 2021, h 13:00 CET

The program is launched by the University of Pisa in partnership with:

- National Research Council - CNR - Scuola Superiore Sant’Anna - Scuola Normale Superiore - Scuola IMT Lucca - Università di Firenze - Università di Modena e Reggio Emilia - Università di Siena - Università di Trento

and in collaboration with:

- Università di Bari - Università di Bologna - Università Cattolica del Sacro Cuore - Università dell’Aquila - Università degli Studi di Napoli L’Orientale - Università di Sassari - Università di Trieste - INDAM (Istituto Nazionale di Alta Matematica “Francesco Severi”) - Open Fiber SpA

This opportunity is part of the Italian National PhD Program in Artificial Intelligence. Overall, PhD-AI.it is made of 5 federated PhD courses that bring together 61 Italian universities and research institutions. The 5 PhD courses share a common basis in the foundations and developments of AI, and each one has an area of specialisation in a strategic sector of AI application. Each PhD course is organized by a lead university, in collaboration with the National Research Council CNR:

- Health and life sciences, Università Campus Bio-Medico di Roma - Agrifood and environment, Università degli Studi di Napoli Federico II - Security and cybersecurity, Sapienza Università di Roma - Industry 4.0, Politecnico di Torino - Society, Università di Pisa

Link to the calls for admissions to all the 5 PhD course are available at http://www.PhD-AI.it

    Language: English

    Approach: Multidisciplinary

    Lenght: 3 Years

    Venue: Pisa, Italy

    Students: 9 + 2

    Start: November 1st

    Requirements: Any Master Degree

    Max Age: Born after 31 Oct 1987

    Grant: 15012   /year

    Lodging: Included

    Meals: Included

Site created and managed by Vittorio Romano

The Ph.D. program in Data Analytics and Decision Sciences (DADS) aims at training highly qualified senior data analysts and data managers capable of carrying out research at universities, international institutions, tech and financial companies, regulatory authorities, and other public bodies.

The program stems from the cooperation between three departments: Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Dipartimento di Ingegneria Gestionale (DIG), Dipartimento di Matematica (DMAT), and the Center for Health Data Science at Human Technopole. It allows the enrolled students to work in a highly interdisciplinary environment with strong connections to international research centers and private companies. The program provides successful candidates with the opportunity to acquire a high degree of professional expertise in specific scientific and technological fields.

The program lasts three years: upon its successful completion and final exam, candidates will be awarded the title of Ph.D. in Data Analytics and Decision Sciences. The first year is devoted to the courses that build the broad competence and the strong interdisciplinary set of skills required by data analytics. The next two years focus on the development of the Doctoral thesis. Students are required to spend at least one semester in a research institution abroad, taking advantage of the network of international collaborations of the three departments involved in the program.

All the students enrolled in the DADS Doctoral Program are supported by scholarships from public institutions and private companies. A call for applications to Ph.D. positions and scholarships is issued around April (see http://www.dottorato.polimi.it/en/looking-for-a-phd/call-for-positions-and-scholarships/ for more details).

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Admissions to 2023-2024 PhD Programmes

Call for pre-selection for citizens of the people’s republic of china - xl cycle - academic year 2024/2025.

Sapienza University of Rome published a call for pre-selection for postgraduate PhD positions with scholarships fund by China Scholarship Council . List of PhD Programmes for which it is possible to apply are indicated in the Appendix CSC XL cycle. The deadline for submission of the application is February 28th, 2024 at 12.00 a.m. (CET).

PLEASE NOTE THAT THIS PRE-SELECTION CALL IS RESEVE ONLY TO CITIZENS OF THE PEOPLE’S REPUBLIC OF CHINA.

Call for pre-selection CSC XL  -  Appendix CSC XL   -   Agreement Sapienza-CSC

Addition PhD Call 2023-2024/ 39th Cycle

Deadline for applications  October 13, 2023, at 23:59 (local time)

National PhD programme - Call 2023-2024/ 39th Cycle

Deadline for applications July 20, 2023, 2 pm local time. 

Dottorato Nazionale in Heritage Science - English version of the call

Dottorato Nazionale in Intelligenza Artificiale - English version of the call

Dottorato Nazionale in Osservazione della Terra - English version of the call

PhD Call 2023-2024/ 39th Cycle

Deadline for applications  June  22, 2023, 2 pm local time. 

Le attività presentate in questa pagina sono finanziate totalmente, o in parte, dai fondi del Piano nazionale di ripresa e resilienza.

L'elenco e le finalità delle attività intraprese dalla Sapienza sono descritte nella pagina dedicata Sapienza per il Pnrr .

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65 Best universities for Data Science in Italy

Updated: February 29, 2024

• Art & Design
• Computer Science
• Engineering
• Environmental Science
• Liberal Arts & Social Sciences
• Mathematics

Below is a list of best universities in Italy ranked based on their research performance in Data Science. A graph of 715K citations received by 29.9K academic papers made by 65 universities in Italy was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. Polytechnic University of Milan

For Data Science

2. University of Bologna

3. University of Pisa

4. Sapienza University of Rome

5. Federico II University of Naples

6. Polytechnic University of Bari

7. University of Milan

8. University of Padua

9. University of Bari

10. Polytechnic University of Turin

11. University of Rome Tor Vergata

12. University of Turin

13. University of Calabria

14. University of Trento

15. University of Catania

16. University of Florence

17. University of Cagliari

18. University of Pavia

19. University of Genoa

20. University of Milano-Bicocca

21. University of Parma

22. University of Salerno

23. University of Modena and Reggio Emilia

24. Free University of Bozen

25. University of Campania Luigi Vanvitelli

26. University of Aquila

27. Polytechnical University of Marche

28. University of Perugia

29. University of Messina

30. University of Udine

31. University of Trieste

32. University of Brescia

33. University of Verona

34. University of Siena

35. Catholic University of the Sacred Heart

36. Mediterranean University of Reggio Calabria

37. University of Salento

38. Bocconi University

39. Ca' Foscari University of Venice

40. Roma Tre University

41. University of Insubria

42. Sant'Anna School of Advanced Studies

43. University of Palermo

44. University of Sannio

45. University of Catanzaro

46. University of Cassino and Southern Lazio

47. University of Camerino

48. University of Ferrara

49. Parthenope University of Naples

50. University of Bergamo

51. University of Sassari

52. University of Eastern Piedmont

53. IUAV University of Venice

54. G. d'Annunzio University of Chieti

55. LUISS University

56. Campus Bio-Medico University of Rome

57. Basilicata University

58. Carlo Cattaneo University

59. Normal School of Pisa

60. Carlo Bo University of Urbino

61. University of Molise

62. University of Tuscia

63. Vita-Salute San Raffaele University

64. University of Foggia

65. University of Macerata

The best cities to study Data Science in Italy based on the number of universities and their ranks are Milan , Bologna , Pisa , and Rome .

Computer Science subfields in Italy

Explore over 200 exciting PhD positions in diverse fields offered by the University of Turin, Italy

📢 Call for Admission to over 200 PhD Positions - 40th Cycle (2024/2025) 🎓

The University of Turin is thrilled to announce the opening of applications for quite large number of PhD positions in the 40th Cycle for 2024/2025 academic year. This is an exceptional opportunity for aspiring researchers to pursue their doctoral studies in diverse fields.

💡 Highlights:

• Assignment of doctoral scholarships funded by the University, Next Gen.EU-NRRP (National Recovery and Resilience Plan), and other funds.
• Join a vibrant academic community and contribute to cutting-edge research.
• Benefit from world-class resources, facilities, and expert supervision.
• Develop your skills and expertise in a supportive and intellectually stimulating environment.

📆 Application Deadline: 20 June, 2024, 12:00pm (noon), CET.

Several academic units will be participating in the call for PhD admission

Comparative Analysis of Institutions, Economics and Law;

Business and Management;

Complex Systems for Quantitative Biomedicine;

Law and Institutions;

Law, the Individual and the Market;

Economy "Vilfredo Pareto";

Medical Physiopathology;

Food System;

Global History of Empires;

Computer Science;

Innovation for the Circular Economy;

Humanities;

Modern Languages and Literatures;

Mathematics;

Experimental Medicine and Therapy;

Molecular Medicine;

Modeling and Data Science;

Neurosciences;

Cultural Heritage and Historical-Artistic Audiovisual and Multimedia Production;

Agricultural, Forest and Food Sciences;

Archeological, Historical and Historical-Artistic Sciences;

Biological Sciences and Applied Biotechnologies;

Biomedical Sciences and Oncology;

Health Sciences: Sociology, Nursing Sciences and Rehabilitation Sciences;

Chemical and Material Sciences;

Earth Sciences;

Pharmaceutical and Biomolecular Sciences;

Psychological, Anthropological and Educational Sciences;

Veterinary Sciences for Food and Animal Safety;

Sustainable Development and Cooperation (SUSTNET);

Technologies for Cultural Heritage (Tech4Culture);

Translational Oncology

For more information and to apply:  https://euraxess.ec.europa.eu/jobs/234024