Call for application 39th cycle

Bando ordinario (expired 22/06/2023)


Educational goals and objectives

DATA SCIENCE
Data Science is an interdisciplinary field of study established in recent years in order to offer the methodological tools and technologies useful for the management and analysis of big data and their exploitation in industry, services, and research. The phenomenon of big data has revolutionized countless sectors of economic and social activity and has profoundly changed research methodologies and technologies in numerous scientific disciplines and applications of great economic and social value. The main objective of the PhD in Data Science is therefore the development of research projects on the fundamental methodological aspects of Data Science (machine learning, statistical and computational analysis of data, management of big data) and on the use of big data in following fields of application:

i) Advanced digital platforms,
ii) Management of urban spaces and environmental resources
iii) Medicine and Health
iv) Economic and Social Analysis.

Data Science receives the decisive contribution of computer science, statistics, engineering, applied mathematics, and academic disciplines that help understand the impact of big data on applications. The Scientific Disciplinary Sectors of the core disciplines of Data Science and of the disciplines related to the fields of application are extensively represented

Specifiche economiche

Tipologia 1: DM118
Transizione digitale Generiche Pubblica Amministrazione Patrimonio culturale
1 1 1 0

Tipologia 2: DM117 Tipologia 3: PE/PNC/CN/TP Tipologia 4:Enti terzi
PE/PNC CN/TP
2 0 0 3

Tipologia 5: Sapienza Senza borsa
2 3

Tematiche, curriculum e competenze specifiche
Themes, curriculum and specific competence

C_PA - DM118 P.A. - non associata a curriculum

- Trasparenza, robustezza, ed efficienza di sistemi di intelligenza artificiale basati sul machine learning per la trasformazione digitale e la pubblica amministrazione
Ente finanziatore: PNRR
Competenze richieste: Apprendimento automatico, analisi algoritmica e statistica dei dati, gestione dei big data
- Transparency, robustness, and efficiency of machine learning-based AI systems for digital transformation and public administration
Founded by: PNRR
Required skills: Machine learning, Algorithmic and statistical data analysis, big data management

C_PNRR - DM118 GENERICHE - non associata a curriculum

- Trasparenza, robustezza, ed efficienza di sistemi di intelligenza artificiale basati sul machine learning per la trasformazione digitale e la pubblica amministrazione
Ente finanziatore: PNRR
Competenze richieste: Apprendimento automatico, analisi algoritmica e statistica dei dati, gestione dei big data
- Transparency, robustness, and efficiency of machine learning-based AI systems for digital transformation and public administration
Founded by: PNRR
Required skills: Machine learning, Algorithmic and statistical data analysis, big data management

C_TD - DM118 TRANSIZIONE - non associata a curriculum

- Trasparenza, robustezza, ed efficienza di sistemi di intelligenza artificiale basati sul machine learning per la trasformazione digitale e la pubblica amministrazione
Ente finanziatore: PNRR
Competenze richieste: Apprendimento automatico, analisi algoritmica e statistica dei dati, gestione dei big data
- Transparency, robustness, and efficiency of machine learning-based AI systems for digital transformation and public administration
Founded by: PNRR
Required skills: Machine learning, Algorithmic and statistical data analysis, big data management

D - DM117 - non associata a curriculum

- Intelligenza Artificiale per il settore zootecnico
Competenze richieste: Conoscenza di Python o di altri linguaggi di programmazione equivalenti; competenze di machine learning e deep learning, in particolare reti neurali; conoscenza ed esperienze nel settore agrifood sono valutate positivamente ma non necessarie.
- Artificial intelligence applied to animal husbandry
Required skills: Knowledge of Python or other equivalent programming languages; machine learning and deep learning skills, in particular neural networks; knowledge and experience in the agrifood sector are positively evaluated but not required.

D - DM117 - non associata a curriculum

- Sviluppo di algoritmi e metodi di AI per l'analisi dei dati acquisiti con tecniche di remote sensing, finalizzati al controllo del territorio e al monitoraggio delle infrastrutture
Competenze richieste: Conoscenza di Python o di altri linguaggi di programmazione equivalenti; competenze di machine learning e deep learning, in particolare reti neurali; conoscenza di remote sensing ed analisi di immagini spettrali sono valutate positivamente ma non necessarie.
- AI algorithms for data acquired via remote sensing methods, for land and infrastructure management
Required skills: Knowledge of Python or other equivalent programming languages; machine learning and deep learning skills, in particular neural networks; knowledge of remote sensing and analysis of spectral images are positively evaluated but not required.

F - ENTI TERZI - non associata a curriculum

- Valutare l'impatto di malattie infettive come COVID-19 nelle scuole e nelle famiglie attraverso la scienza dei dati e la modellizzazione
Ente finanziatore: FONDAZIONE ISI
Competenze richieste: Apprendimento automatico, modellazione matematica, analisi coputazionale dei dati.
- Assessing the impact of infectious diseases such as COVID-19 in schools and household through data science and modelling
Founded by: FONDAZIONE ISI
Required skills: Machine learning, mathematical modelling, computational data analysis.

F - ENTI TERZI - non associata a curriculum

- Tecniche di Deep Learning su dati multimodali per applicazioni a sistemi di ricerca e raccomandazione
Ente finanziatore: ISTI - CNR
Competenze richieste: Apprendimento automatico, Apprendimento profondo, Ricerca nel Web, Information Retrieval, Sistemi di raccomandazione
- Deep Learning techniques on multimodal data for search and recommender system applications
Founded by: ISTI - CNR
Required skills: Machine learning, Deep learning, Web search, Information Retrieval, Recommendation Systems

F - ENTI TERZI - non associata a curriculum

- non indicata
Ente finanziatore: AMAZON
Competenze richieste: nessuna competenza specifica richiesta

Il candidato sceglierà una tematica in fase di presentazione della candidatura on line

Admission Procedure

Qualifications assessment The Admission Committee assigns to
each candidate a maximum score
of 60 points. Scores are assigned
according to the evaluation criteria
defined by the Board of Faculty of the
Ph.D. Program, and reported below:
- up to 30 points for the evaluation of the
curriculum (including the academic
career and any other qualifications), the
letters of recommendations supporting
the candidate and the publications
presented by the candidate;
- up to 30 points for the research
proposal submitted by the candidate. In
particular, the commission evaluates the
description of the state of the art, the
originality and
the innovative nature of the proposal,
the clarity and completeness of the
objectives, the
methodologies and the potential results,
the relevance of the proposal with
respect to the themes and the objectives
of the Ph.D. program. Candidates
obtaining a minimum score of 36/60 in the
evaluation of qualifications and of the
research proposals are invited for an
interview.
Oral interview The Admission Committee assigns a
maximum of 60 points to each candidate
admitted to the interview. A score of at
least 36 is required for the admission.The
interview is in English, and is aimed
to assessing the candidates' knowledge,
skills, and aptitude to carry out research
in the scientific areas of Data Science.
The interview also includes a presentation with slides
of the research proposal prepared by
the candidate and of personal
motivations for applying for a Ph. D.
position. The duration of the interview is
at most 30 minutes (the presentation of
the research proposal by the candidate
must be no longer than 15 minutes).
The minimum overall score for admission to
the Ph.D. in Data Science is 72/120.
language INGLESE

contacts and info Email: dottoratods@diag.uniroma1.it Web: https://phd.uniroma1.it/web/DATA-SCIENCE_nD3565_IT.aspx

Curriculum studiorum

Graduation date and grade of the Master's degree
detailed list of exams including completion dates and scores of Masters's degree
Graduation date and grade of the Bachelor’s degree
detailed list of exams including completion dates and scores of Bachelor's degree
History of Scholarships, Research Grants (or similar)
Certificates of Foreign Languages
Certificates of participation in post-graduate university courses
certificates of Participation in research groups
certificates of Participation in internships
Other University Awards/Degrees (e.g.: awards in competition, second degree)
Computer skills

Required documentation

§ research project
mandatory
8000 characters, spaces included, bibliographic references excluded. , the file must be uploaded within 22/06/2023 ore 14:00 (ora italiana)
§ first letter of introduction (by a teacher)
optional, the letter must be uploaded by the referee by il 30/06/2023 ore 14:00 (ora italiana)
§ second letter of introduction (by a teacher)
optional, the letter must be uploaded by the referee by il 30/06/2023 ore 14:00 (ora italiana)
§ Curriculum Vitae et Studiorum
mandatory, the file must be uploaded within 22/06/2023 ore 14:00 (ora italiana)

Language Skills

the candidate must know the following languages
ENGLISH

Exam Schedule

Qualifications assessment
day03/07/2023
notesnone
publication on notice boardNO
publication on the web siteYes
web sitehttps://phd.uniroma1.it/web/DATA-SCIENCE_nD3565.aspx
date of publication04/07/2023
contactsdottoratods@diag.uniroma1.it

Oral interview
day06/07/2023
notesLe prove continueranno il giorno successivo alle ore 09:00 ove necessario. Il Diario delle prove orali sarà pubblicato il 04/07/2023
time09:00
classroomB101 - Dipartimento di Ingegneria Informatica, Automatica, e Gestionale
addressVia Ariosto 25, 00152, Roma
publication on notice boardNO
publication on the web siteYes
web sitehttps://phd.uniroma1.it/web/DATA-SCIENCE_nD3565.aspx
date of publication10/07/2023
contactsdottoratods@diag.uniroma1.it

Evaluation scale

file:visualizza il file
file (eng):visualizza il file (eng)

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