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Department of Economics, Management and Statistics  DEMS

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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

Administration Office: Mrs. Clara Sereni

NEW!! Milan PhD Economics Workshop 2024 - 2nd edition

The ECOSTATDATA PhD students are happy to announce the second edition of the Milan PhD Economics Workshop - 2nd edition , that will be held at the premises of the University of Milano-Bicocca, on September 16, 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 available here .

For details you can contact the local organizers:

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

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 - IV) - 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 IV term teaching activities start on 9 September 2024 and end on 28 October 2024. 

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

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

- Statistical Modelling II (Instructor: Drs. D. Spinelli and G. Zaccaria , University of Milano-Bicocca)

- Statistical Modelling III (Instructor: Dr. L. Brusa , University of Milano-Bicocca)

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

Calendar 2024-2025

The I term teaching activities start on October 23, 2024 and end on December 20, 2024. The I term exam session starts on January 7, 2025 and ends on January 14, 2025.

The II term teaching activities start on January 20, 2025 and end on April 4, 2025. The II term exam session starts on April 11, 2025 and ends on April 16, 2025.

The III term teaching activities start on April 22, 2025 and end on July 4, 2025. The III term exam session starts on July 14, 2025 and ends on July 18, 2025.

The IV term teaching activities (curriculum STAT only) start on September 8, 2025 and end on October 26, 2025. The IV term exams are scheduled at the end of each module.

The resit exams (all curricula) are typically scheduled in the period September-October 2025. 

XL cycle - Teaching activities - Year I (term I) - courses

Note:  The list of I term courses will be published soon and the timetable will be shared online (via Google Calendar) with all students officially enrolled in the PhD program.

-  Mathematics – Linear algebra  (Instructor:  A. Mainini , Catholic University of Milano)

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

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

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

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

The timetable is available here .

-  Mathematical Analysis I  (Instructor: E. Miglierina , Catholic University of Milano)

-  Mathematical Analysis II  (Instructor: C.A. De Bernardi , Catholic University of Milano)

-  Mathematical Analysis III  (Instructor: F. Battistoni , Catholic University of Milano)

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

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

-  Architecture for Big Data Processing Lab  (Instructor: A. Galli , University of Napoli)

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

Reading groups (rgs) offered in academic year 2024-25 (xxxix cycle – ii year) for the curriculum economics (eco):.

I term (October 2024 – December 2024) and II term (January 2025 – April 2025)

Reading Groups (RGs) offered in academic year 2024-25 (XXXIX cycle – II year) for the curriculum Statistics (STAT):

I term (November 2024 – December 2024) and II term (January 2025 – April 2025)

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

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

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

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

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

Reading Groups (RGs) offered in academic year 2024-25 (XXXIX cycle – II year) for the curriculum Big Data & Analytics for Business (BIDAB):

I term (November 2024 - December 2024) and   II term (January 2025 – April 2025)

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

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

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RICEVI LA MINIGUIDA

Statistical sciences.

The PhD Course in Statistics aims at training specialists in the fields of data management and analysis and leads to a wide range of career opportunities, both in academic and research institutions.

  Find out more

The doctoral programme aims to train international specialists in the fields of data management and data analysis. The programme leads to a wide range of career opportunities, both at academic level and in other highly qualified research institutions. The study project of the doctoral course is carried out according to the following steps. Start of activities. On arrival, students are informed of the regulations and the course programme for the first few months. Each student is assigned a tutor, who is available to help and guide them in their choice of research topic for the second and third year. First-year programme. The basic first-year programme comprises a core of five compulsory courses covering advanced mathematics, probability theory, computer programming, statistical theory and modelling. In addition, a number of specialised modules are offered, covering a range of more advanced topics. A side objective of the courses during the first year is to train students in group work, seminar presentations and the preparation of scientific papers. At the end of the first year, the Academic Board assesses doctoral students for admission to the second year. Admission is conditional on achieving a satisfactory level in the first year's activities. For each of the five core modules, assessment is based on a final examination. By September of the first year, students admitted to the second year propose to the Academic Board their research programme to be developed during the second and third years. Students may join local research groups or start an independent research project. The PhD programme and the Department of Statistical Sciences can provide a suitable research environment for this. Broad areas of research include: statistical methodology and its applications; statistical and econometric methods; social statistics; demography. After approval of the project, the Academic Board assigns a supervisor to each PhD student. Research programme in the second and third year. The research activity in the second and third year is the distinctive feature of the PhD programme and is aimed at achieving independent research capabilities. During the second and third years up to 12 months may be spent at a university or other highly qualified institution abroad. Students are strongly encouraged to include a period of research abroad in their programme, taking advantage of the national and international collaborative networks of the members of the Doctoral Board. The result of the research must be presented as a thesis containing original scientific results relevant to the field of statistics and its applications.

  Other information

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Admissions to 2024-2025 PhD Programmes

Call for applications for admission to phd programmes  of national interest (din) - academic year 2024/2025 - 40th cycle .

Call for PhD programmes of National Interest (DIN) in HERITAGE SCIENCE – EARTH OBSERVATION – ARTIFICIAL INTELLIGENCE – PEACE STUDIES has been published.

Applications must be submitted by 10 July 2024 at 23:59 pm (local time) without any exception.

Call for applications DIN PhD programmes 40th cycle - 2024_2025 (pdf)  Applications Submition Tutorial - 40th cycle_2024_2025 (pdf)

CALL FOR APPLICATIONS FOR ADMISSION TO PHD PROGRAMMES - Academic Year 2024/2025 - 40th Cycle 

Sapienza University of Rome announces the following exam-based open call for admission to the 40th Cycle of PhD Programmes. The legal duration of all PhD programmes is three years.  Applications must be submitted by 20 June 2024 at 23:59 pm (local time) without any exception.

Call for applications 40th cycle - 2024_2025  (pdf) Applications Submition Tutorial - 40th cycle_2024_2025 (pdf)

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

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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Dottorato di ricerca

The phd course, partner departments, duration, admission, scholarships, study programme, research areas, career opportunities.

The doctoral programme in Statistical Sciences at University of Padua was introduced in 1984, and was among the first Ph.D. programmes in Italy.

Starting in the academic year 2004/2005, the Department of Statistical Sciences of the University of Padua became the administrative home of the Ph.D. in Statistics.

The new programme replaced two previous research doctoral programmes in Statistics and in Statistics Applied to the Economic and Social Sciences.  

The Ph.D. Course aims to provide an advanced training to support both applied and theoretical research, encouraging independence of both study and research. For a short description of the Course activities see the notice .

Partner Departments in the Ph.D. programme comprise:

 -  Department of Environmental Sciences, Computer Sciences and Statistics, University Ca' Foscari of Venice

 -  Department of Economics and Statistical Sciences, University of Udine

The PhD programme in  Statistical Sciences has an expected length of 3 years. About 8 students enter the programme every year. The School aims at selecting the best candidates applying from all over the country as well as from abroad. Admission is by competition which is open to people of all ages and citizenship who already have a Diploma di Laurea (vecchio ordinamento) or Laurea Specialistica/Magistrale (nuovo ordinamento), issued in Italy, or an academic qualification of equivalent level issued by a foreign university and recognized by the academic authorities, or by interuniversity agreement of cooperation and mobility.

Basic quantitative prerequisites are assumed.

Competitions are held annually, with details being published within the Spring of each year. Courses start the following October. 

For full information, see the Admission section and the page of University of Padua competition announcement . 

All positions are supported by scholarships from the University of Padua or from other institutions (awarded on the basis of qualifications and oral examination).  The gross annual amount of the scholarship is 16,243.00 euros , in accordance with the amount set by the D.M. MUR n. 247 of 23/01/2022. For periods of study abroad (up to 12 months) the scholarship value is increased by 50%. Moreover, an annual research budget equal to 10% of the gross annual amount of the scholarship is available for the 2nd and 3rd year. The China Scholarship Council (CSC) , offers n. 1 scholarship to candidates from the People's Republic of China .

See the Admission section and the page of University of Padua competition announcement  for updates. Please, follow carefully the instructions for application in the announcement. For any additional information, please contact [email protected] .

The first year is devoted to courses, both core courses on Functional Analysis, Probability Theory, Programming Methodologies for Data Analysis, Statistical Theory and Statistical Modelling as well as short, more specialised, courses on topics such as: Time Series, Survival Analysis, Nonparametric Statistics, Survey Methodology, etcetera. Lectures are given in English . Students can also follow some upper-division courses (Laurea Magistrale) at the Department of Statistical Sciences or other Departments at the University of Padova .   Attendance to all courses is compulsory .

At the end of the first year, PhD candidates propose to the Course Collegiate their own research programme to be carried over during the second and third year. They can either join local research groups at Padova or partner departments or start an independent research. The Course Collegiate assigns a supervisor to each PhD student. Original research results should be presented in the PhD thesis, to be discussed both in front of the Course Collegiate and (subject to positive evaluation by the Collegiate) in front of a Committee composed of Italian and foreign experts on the thesis topic. Intermediate results at the end of the second year are to be presented as a department seminar. During the second and third year up to 12 months can be spent at a University or other highly qualified institution abroad. In this case the grant is 50% more, for the whole period spent abroad. The thesis can be written either in Italian or in English.

For an idea of possible research subjects, see the section Alumni and the research pages of the Department of Statistical Sciences , University of Padua and of partner departments. 

Broad areas of research include:  

Statistical methodology and its applications . Methodological aspects range from statistical models to inference and computational issues. Applications may concern a variety of fields such as technology, industry, finance, biology, medicine, environmental studies, etc.

Statistical methods and applications in Economics . In particular: time series analysis, forecasting, statistical methods for labour economics and evaluation of public policies.

Social Statistics . In particular, survey methodology, models for individual ad aggregated data, segmentation techniques, multilevel models.

Demography . In particular, population structure and dynamics, statistical analysis of demographic behaviours and policies.

As a general objective, the Course aims at training specialists at an international level in the fields of data management and analysis. The programme leads to a wide range of career opportunities, both at the academic level and in other highly qualified research institutions. See the section Alumni for details on the current occupation of PhDs from The Department of Statistical Sciences starting from 1987. 

Students having completed one of the doctoral programmes in Statistics at Padua now have positions in a wide range of academic, public and private institutions, both in Italy and abroad.

Tab. 1   Some statistics related to the professional results of the research's doctors of the PhD School/Course of the Department of Statistical Sciences of the University in Padua

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PhD programmes

Are you interested in a PhD programme?

PhD Programmes attendance at the University of Bologna is conditional upon passing an examination. The provisions governing the selection procedure are laid down in the Call for applications and its attachments.

• University of Bologna PhD programmes
• Other phd programmes the University of Bologna is involved in
• Marie Skłodowska-Curie Actions – COFUND FutureData4EU Programme - 53 PhD positions to train new Big Data talents
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• How to apply for a PhD programme
• FAQ - Frequently Asked Questions on the PhD Call for applications and selection procedures

Are you a PhD candidate at Unibo?

Consult the Intranet Website for information concerning your career, the opportunities and services available for PhD candidates enrolled at the University of Bologna: PhD grants, tuition fees, exemption, agreements, Co-tutorship "cotutelle" agreement, mobility, requirements for staying abroad, absences, thesis, final exam, etc.

• Intranet Website (restricted access with @unibo credentials only)
• Regulation Governing the PhD Programme Degrees (Third Cycle Programme)
• Internationalisation of the PhD programme
• Certificates for PhD candidates

PhD programmes with firms and bodies

The PhD programme degree may well be a preferential tool for scientific collaboration with the University of Bologna and for exchanging the knowledge and competencies that underpin innovation.

• PhD programme with companies and public institutions
• PhD Apprenticeship Programmes

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PhD Programmes brochure [.pdf 525 KB]

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Fully Funded PhD Scholarships

to join its elite Doctoral Programs. PhD Programs Offering 50 Fully Funded Scholarships: Coordinator: prof. Carlo Rescigno Info: acma[at]ssmeridionale.it Coordinator: prof. Michelino De Laurentiis Info

PhD : Dynamical properties of magnetic nanostructures

The University of Perugia offers in the next call for admission to the 40th Cycle in Ph.D., a 3-year position in Physics to carry out research on “Dynamical properties of magnetic nanostructures” within the group of Dr. Gianluca Gubbiotti and Dr. Silvia Tacchi. The position is funded by the...

PhD position

We are looking for candidates for a PhD position in Geophysics at University of Roma Tre, Italy. The research topic is the Constraints on crustal physical properties from joint inversion of multiple

Unlock Your Potential with a PhD at Scuola Superiore Meridionale

Qualification Type: PhD Location: Naples - Italy Funding for: EU Students, International Students Scholarship amount: €19,000 per year (before social contributions) for up to 4 years, provided in

PhD position: Numerical downscaling for climate change adaptation

A PhD position is available in the Atmospheric Physics group at the University of Bologna. The fellowship is part of the FutureData4EU project, funded by the European Marie Curie (MSCA) COFUND

Postdoc to investigate molecular mechanisms in the field immune-rejuvenation for drug discovery

In the compelling and unstoppable research in favor of cellular rejuvenation, Sentcell LTD is looking for PhD or Postdoc Researchers to investigate molecular mechanisms and innovative therapies

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Doctoral research programmes are postgraduate university programmes providing specialised training to those who wish to undertake advanced research.

The academic pathway includes the definition and development of a  research project - through advanced teaching programmes and individual in-depth studies, in many cases also involving cultural exchanges with other Countries - and results in the production of a thesis based on original experimental work .

These are 3 or 4 year programmes .   Attendance is mandatory.

Admission requirements

To be admitted to a doctoral programme applicants must:

• hold a Master's degree or a suitable equivalent foreign qualification  (or a Diploma for those who graduated before Ministerial Decree 509/99 entered into force);
• pass the admission tests , as indicated in the calls for application published annually by the University;
• submit within the first year of enrolment a B2 level language certificate , as indicated in the call or, for candidates of the University of Milan, submit a language statement from the University Language Centre - SLAM

Qualification awarded an employment prospects

The Doctoral degree is awarded on passing the final exam, during which students defend their doctoral thesis. The degree allows access to the world of scientific research, mainly in the academic world but also in independent research centres  and production facilities.

• PhD Regulation
• Regolamento di accreditamento delle sedi e dei corsi di dottorato e criteri per…

During the meeting on April 12th 2022, the Academic Senate established that the DM 226/2021, transposed into the 2022 PhD Regulation, will be applicable starting form the XXXVIII cycle.

• Examination boards list (PhD)

Access to a doctoral programme (PhD) is through a public competition: find out about application deadlines and procedures. 

Doctoral programme course catalogue, transversal competences, exams and enrolment renewal.

Payment procedures, welfare, compatibility with other scholarships or work activities.

International mobility o pportunities - Thesis co-supervision and Marie Skłodowska-Curie Actions.

The doctoral dissertation: admission application, deadlines, procedures, dates and exam boards.

The funding for doctoral scholarships can come from a range of companies and organisations.

Universities can institute PhD programmes with the opportunity to allocate a share of the available places to employees of companies engaged in highly-skilled activities

Temporary suspension of doctoral studies

How to withdraw from a doctoral programme for personal reasons. 

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University of Turin

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The Ph.D. (Doctor of Philosophy, “Dottorato di ricerca”) is the  highest university degree  in Italy, aiming to provide the necessary knowledge, skills and research abilities to engage in  top-quality research activities  in universities, public institutions, or private bodies.

A yearly call for applications is launched to be selected for a Ph.D. Program at the University of Turin. Applicants must hold a postgraduate degree (or a foreign equivalent).

3 or 4 years  are normally needed for the completion of a doctoral program. Award of the Ph.D. is conditional upon a successful oral defense of own’s dissertation in front of an Examination Committee.

PhD programs at the University of Turin are designed and offered within the Doctoral School.  The Doctoral School coordinates the Ph.D. programs and runs joint activities.

All information for prospective PhD Candidate, as requirements to access a PhD program and the yearly call for applications, and for current PhD candidates about their PhD path, financial aid, ect, is available at the  Doctoral School website .

UniTo and the Doctoral School aim at excellence in research and training by promoting the  internationalisation  of their students, PhD programs and research activity through mobility plans, scientific cooperation networks, participation to competitive bidding. To learn more about the international dimension of UniTO and its PhD Programs, please read the related page about the  Internationalisation of PhD courses .

The PhD also represents a preferred solution for scientific collaboration between the University of Turin, non-academic institutions and private companies for exchanging knowledge and expertise in innovation. To learn more about opportunities of  collaborations between the private sector, non-academic institutions and PhD Programs  read the page dedicated to  PhD with companies and institutions .

Discover the full list of PhD programmes

International PhD programmes for supporting the mobility of PhD students

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Math PhD and Statistics Programs in Italy

PhD in Mathematics and Statistics programs award graduate students the highest level of scholarship in their field. In addition, they typically indicate they have made original research contributions. For students aspiring to an academic position or research-oriented role as an analyst, the Ph.D. is considered the academic requirement. If you aspire to potential upward mobility in a current job, a math or statistics Ph.D. warrants consideration.

PhD in Mathematics and Statistics: Program Basics

At the doctorate level, many Ph.D. in Mathematics and Statistics programs focus either on the pure or applied forms of their disciplines (e.g. applied statistics, pure statistics, applied mathematics, pure mathematics). Mathematics is a critical component of natural science, engineering, medicine, finance and social sciences.

Prospective doctoral students may be required to have a bachelor’s degree, letters of recommendation, qualifying GRE scores, and any prerequisite math, statistics or computer science courses. Also, coursework in biology, chemistry or the health sciences may be useful if you plan on entering the pharmaceutical industry ( BLS ). The culmination of a doctorate degree is a student-submitted dissertation.

FACT: 20% of Statisticians and 50% of mathematicians hold a doctorate degree

Types of Statistics and Math Ph.D. Programs

Ph.D. Math and Statistics Degrees are offered in different subjects, depending on your professional or academic interests. If you refer to the GradSchools.com directory these are simplified into:

• Actuarial Science & Financial Engineering
• Mathematics
• Statistics & Data Analytics

Actuarial Science & Financial Engineering Ph.D. Programs

Actuarial science became a formal mathematical discipline in the late 17th century.

Ph.D. in Actuarial Science Programs might focus on the application of mathematics and statistics to assess risk in insurance, finance and other industries. Financial Engineering is multidisciplinary in that it draws from applied mathematics (financial mathematics), computer science, statistics, and economic theory.

Mathematics Ph.D. Programs

PhD in Mathematics Programs may focus on either pure mathematics or applied mathematics or may have components of both. Pure mathematics focuses on arithmetic, algebra, geometry and analysis. Applied mathematics is a branch of mathematics and deals with mathematical methods that are utilized in science, engineering, business, computer science, and industry. Applied mathematics refers in these cases to professional specialty.

Statistics & Data Analytics Ph.D. Programs

PhD in Statistics & Data Analytics programs may focus on mathematical variances of collecting, analyzing, interpreting, presenting and organizing data; mathematical statistics is the application of mathematics to statistics (e.g. probability).

Popular Schools with Math PhD Programs in Italy

in Italy PhD in Mathematics and Statistics Program Options: Online or On Campus

Deciding on a doctorate program involves different aspects; the program itself and the learning delivery that aligns with your lifestyle. One is not necessarily better than the other; choosing between an online or on-campus program is likely part personal, and part availability of options.

Certainly, if you prefer in-person learning or live really close to your graduate school, an onsite program may be preferred. You can initiate a search on GradSchools.com to find graduate programs by city, state or country. On the other hand, if you are a busy working professional or live geographically too far from graduate school, you may decide an online math or statistics Ph.D. is the best option.

Some of the sponsored listings you can explore may include: Ph.D. in Pharmacology, Doctor of Computer Science – Big Data Analytics (Executive Format), Ph.D. in Statistics , Ph.D. in Mathematics. When you find a program that interests you, use the easy navigation to request more info or visit the school’s website.

FACT: Job titles for mathematicians include Computational Scientist, Director of Quantitative Research, Lead Simulation Modeling Engineer, and Research Scientist ( O’Net )

Accreditation

There are two types of accreditation: program and institution. Program accrediting agencies vary depending on subject; ABET is the accrediting agency for programs in applied science, computing, engineering, and engineering technology and are recognized as an accreditor by the Council for Higher Education Accreditation. The CHEA has a database listing accredited programs and institutions.

Take the Next Step

Whether you are seeking a new career such as teaching math at the university level, or interested in potentially taking on a more advanced or senior position in your current job setting, a PhD in Mathematics and Statistics program may be what takes you to that next step. The Bureau of Labor Statistics projects an extremely positive employment outlook for mathematical science and statistician occupations from 2022 to 2032, so now might be a really favorable time to consider a doctoral degree.

Bureau of Labor Statistics

GradSchools.com offers 202 Graduate Schools with Math PhD and Statistics Programs in Italy

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National university, baylor university, university of maryland baltimore county, brigham young university, syracuse university, emory university, university of illinois at chicago, university of pittsburgh, cornell university, northwestern university school of continuing studies, carnegie mellon university, browse graduate programs.

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PhD in Economics and Finance

This page has been developed for PhD applicants who have been recommended for admission to the PhD Program in Economics and Finance at Bocconi University in Milan.

Most likely you are considering several graduate programs. We believe that if you learn about our program, Bocconi University, and the city of Milan you will be able to make an informed decision about where to attend graduate school. Of course, we think Bocconi is the best choice you can make.

We have put together information about Visit Day, the Bocconi University in Milan. After reviewing the information if you still have questions, please contact Angela Baldassarre  ( [email protected]) .

   Visit Day Schedule ( work in progress)

     - Overnight accommodation : Hotel d'Este****

     - “Beer get together”  ( Venti136 lounge bar at Roentgen's corner )         How to participate remotely Contact PhD Students’ Representatives for information

Info for prospective students: Handbook (full set of rules)

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PhD Projects, Programmes & Scholarships in Italy

Fully funded phd positions in life sciences - 2025 winter recruitment, funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

Germany PhD Programme

A German PhD usually takes 3-4 years. Traditional programmes focus on independent research, but more structured PhDs involve additional training units (worth 180-240 ECTS credits) as well as placement opportunities. Both options require you to produce a thesis and present it for examination. Many programmes are delivered in English.

PhD in Systems Medicine

Italy phd programme.

An Italian PhD usually takes 3-4 years and consists of some taught units as well as research towards your thesis. This will be examined at a public defence, rather than a private viva voce. Some programmes are taught in English.

Fully-funded four-year PhD in History and Civilisation

Social sciences research programme.

Social Sciences Research Programmes present a range of research opportunities, shaped by a university’s particular expertise, facilities and resources. You will usually identify a suitable topic for your PhD and propose your own project. Additional training and development opportunities may also be offered as part of your programme.

SELF-RE-PREG: Self-Sensing Interleaving for Recycled Prepreg

Phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Competition Funded PhD Project (Students Worldwide)

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Fully-funded four-year PhD in Political and Social Sciences

Fully-funded four-year phd in law, law research programme.

Law Research Programmes present a range of research opportunities, shaped by a university’s particular expertise, facilities and resources. You will usually identify a suitable topic for your PhD and propose your own project. Additional training and development opportunities may also be offered as part of your programme.

Fully-funded four-year PhD in Economics

Fully funded phd positions at the imt school for advanced studies lucca.

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PhD—Doctor of Philosophy in Nursing Science

A Holistic View of Nursing Science

The UW School of Nursing Doctor of Philosophy in Nursing Science (PhD) program faculty are global leaders in nursing science. Students from all over the world create a community of scholars and learn from expert researchers. Our expert nursing scientists provide mentorship tailored to student needs using technology-driven instruction.

The UW PhD program is ideal for students with diverse backgrounds who want to become premier leaders in nursing research.

We maintain a holistic view of nursing science and our faculty’s scholarship represents a broad array of research projects and innovative methodologies. Some examples include:

• Symptom science
• Laboratory sciences
• Health promotion and disease management across the lifespan
• Policy analysis and advancement of cost-effective health care systems
• Health equity
• Innovative methodologies

Hear from #HuskyNurse Sarah McKiddy and her experience with the PhD program.

A Community of Scholars

We welcome applicants with a baccalaureate degree in any field. We encourage post-baccalaureate students, as well as most master’s and DNP students who are ready to begin intensive study of nursing science, to apply.

UW PhD students have a wide range of academic backgrounds, including:

• Public health
• Complementary and alternative medicine
• Healthcare systems and policy
• Music therapy

Our PhD program is highly individualized to create independent thinkers with demonstrated ability to articulate, investigate, and report on original research that substantially contributes to health care knowledge.

The flexible program reflects the enormous scope and impact of the latest nursing research and policymaking. Although the PhD curriculum can be completed in three years of full-time study, individual student program lengths are expected to vary.

Once offered admission as a PhD student, you are matched with a faculty adviser. Your adviser provides guidance and helps answer questions until you select a supervisory committee chair (between years one and three). Your chair then serves in an advisory capacity and as the supervisor for your dissertation.

View the PhD Sample Curriculum Grid PhD Required and Elective Courses

• Optional: Statistics Concentration

The first year of the full-time PhD program and the first two years of part-time focus on required core courses emphasizing nursing philosophy, conceptual frameworks, theory, and research methodologies.

In addition, students complete their statistics sequence and may take selected electives or engage in research or teaching practicum. Students complete the preliminary exam at the end of the first year of full-time study and the end of two years of part-time study.

Students who enter the PhD program with a BSN can earn an MS in Nursing Science during the program following successful completion of the first year of the PhD program and the preliminary examination.

After the first year of full-time study and the first two years of part-time study, courses and other scholarly activities are determined by you and your supervisory committee to comprise your individual program of study. The supervisory chair is a School of Nursing graduate faculty member whose research approach, interest, or research methods align with your proposed dissertation project.

You and your chair complete the supervisory committee by selecting faculty members from the School of Nursing, the UW at large, and other appropriate experts outside the University. This committee approves your program of study and guides you through the general examinations, dissertation research, and final defense.

The Nursing Science Statistics Area of Concentration requires a minimum of 14 credits of advanced statistical methods coursework above and beyond the 10 credits of statistics required for the PhD degree. Of these, two credits are fulfilled by the CSSS Seminar, and a minimum of 12 credits are fulfilled by at least four advanced statistics courses, three of which must come from the list of CSSS courses approved for the concentration.

All courses taken as part of the statistics concentration must be graded.

However, a 500-level course that is graded credit/no credit may be considered for inclusion in your Area of Concentration proposal if:

• In the course, you submit an individual project (i.e., not a group project); and
• Major course assignments are submitted to your Supervisory Committee for review

CSSS seminar: minimum two credits

A minimum of two quarters in CS&SS is required. The most up-to-date course descriptions for statistics course options can be found on the CSSS website .

Approved CSSS advanced statistics course options

The most up-to-date course descriptions for statistics course options can be found on the CSSS website .

Variation in area of concentration coursework

One advanced methodological course in nursing, political science, psychology, public health, sociology, statistics, economics, educational psychology, social welfare, and other related fields may be considered as part of the Nursing Science Statistics Area of Concentration four course requirements if it contributes to the overall coherence of the student’s program of study and is consistent with the student’s research goals.

This course would be in addition to the ten credits of statistics courses required for the PhD in nursing.

Admissions Requirements

• An in-process or earned bachelor’s degree from an accredited university in any discipline
• A minimum cumulative Grade Point Average (GPA) of 3.0, or a 3.0 GPA for the last  60 semester/90 quarter graded college/university credits
• Meet the  Essential Behaviors for PhD Students
• International applicants   who plan on taking clinical nursing courses  must have an active, unrestricted U.S. RN license  at the time of application
• If your native language is not English,  proof of proficiency in English  (reading, writing, and comprehension)