Call for application 38° cycle


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 in the Board of Faculties of the Doctoral Program in Data

Specifiche economiche

Tipologia 1: DM118
Transizione digitale Generiche Pubblica Amministrazione Patrimonio culturale

Tipologia 2: DM117 Tipologia 3: PE/PNC/CN/TP Tipologia 4:Enti terzi
PE/PNC CN/TP
5 0 4

Tipologia 5: Sapienza Senza borsa
3 2

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

A - PNRR351 TECHNOPOLE - non associata a curriculum

- Roma Technopole - Digital Transition, FP2 - Energy transition and digital transition in urban regeneration and construction
Competenze richieste: nessuna competenza specifica richiesta
- Roma Technopole - Digital Transition, FP2 - Energy transition and digital transition in urban regeneration and construction
Required skills: no specific skill required

D - PNRR352 - non associata a curriculum

- Machine learning e manutenzione predittiva per soluzioni sulla mobilità del futuro
Competenze richieste: nessuna competenza specifica richiesta
- Machine learning and predictive maintenance for mobility solutions of the future
Required skills: no specific skill required

D - PNRR352 - non associata a curriculum

- Sensor fusion per la mobilità del futuro: applicazioni automobilistiche dell' Intelligenza artificiale per veicoli e infrastrutture stradali
Competenze richieste: nessuna competenza specifica richiesta
- Sensors fusion for the mobility of the future: automotive application of Artificial Intelligence for vehicle and road infrastructure
Required skills: no specific skill required

D - PNRR352 - non associata a curriculum

- Metodi e rappresentazioni in Graph Machine Learning ed elaborazione del linguaggio naturale per applicazioni biomediche
Competenze richieste: nessuna competenza specifica richiesta
- Methods and Representations in Graph Machine Learning and Natural Language Processing for Biomedical Applications
Required skills: no specific skill required

D - PNRR352 - non associata a curriculum

- Elaborazione ottimale dei dati per applicazioni di osservazione della Terra da sensori iperspettrali
Competenze richieste: nessuna competenza specifica richiesta
- Optimal Data Processing for Earth Observation Applications from Hyperspectral Sensors
Required skills: no specific skill required

D - PNRR352 - non associata a curriculum

- Apprendimento dai dati di modelli ad agenti per l’evoluzione della dinamica delle opinioni
Competenze richieste: nessuna competenza specifica richiesta
- Learning Agent-Based Models of Opinion Dynamics from Data
Required skills: no specific skill required

E - INFRASTRUTTURE - non associata a curriculum

- Fairness, Bias, and Explainability of ML systems
Ente finanziatore: SoBigData
Competenze richieste: nessuna competenza specifica richiesta
- Fairness, Bias, and Explainability of ML systems
Founded by: SoBigData
Required skills: no specific skill required

E - INFRASTRUTTURE - non associata a curriculum

- Explainability in Graph Neural Networks
Ente finanziatore: SoBigData
Competenze richieste: nessuna competenza specifica richiesta

F - ENTI TERZI - non associata a curriculum

- Robustezza di algoritmi di deep learning ad attacchi adversarial e corruzioni nonlineari
Ente finanziatore: LEONARDO Spa
Competenze richieste: nessuna competenza specifica richiesta
- Deep learning Robustness to adversarial samples and nonlinear corruptions
Founded by: LEONARDO Spa
Required skills: no specific skill required

F - ENTI TERZI - non associata a curriculum

- Geometric Deep Learning per la Comprensione dei Terremoti
Ente finanziatore: ISTITUTO NAZIONALE DI GEOFISICA E VULCANOLOGIA
Competenze richieste: nessuna competenza specifica richiesta
- Geometric Deep Learning for Understanding Earthquakes
Founded by: ISTITUTO NAZIONALE DI GEOFISICA E VULCANOLOGIA
Required skills: no specific skill required

F - ENTI TERZI - non associata a curriculum

- Modellazione dell'accoppiamento litofera-atmosfera-ionosfera con tecniche di ML
Ente finanziatore: ISTITUTO NAZIONALE DI GEOFISICA E VULCANOLOGIA
Competenze richieste: nessuna competenza specifica richiesta
- ML based modelling of the lithosphere-atmosphere-ionosphere coupling
Founded by: ISTITUTO NAZIONALE DI GEOFISICA E VULCANOLOGIA
Required skills: no specific skill required

F - DOTTORATO INDUSTRIALE - non associata a curriculum

- Reinforcement learning per l’ottimizzazione della supply chain
Ente finanziatore: MACAI SRL
Competenze richieste: nessuna competenza specifica richiesta
- Reinforcement learning for supply chain optimization
Founded by: MACAI SRL
Required skills: no specific skill required

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 proposal 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 discussion
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 45 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 ITALIANO

contacts and info Email: dottoratods@diag.uniroma1.it. Referente Dottorato presso il Dipartimento di Ingegneria Informatica, Automatica, e Gestionale Antonio Ruberti: Stefano Leonardi: 06 77274022 Sito Web: https://phd.uniroma1.it/web/DATA-SCIENCE_nD3565_IT.aspx
more info The goal of the admission exam is to assess the ability of the
candidate to work in the area of Data Science of the research project
that he presented and the motivation to enter the PhD program.

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
The project should not exceed the maximum length of 8000 characters
(spaces included)., the file must be uploaded within 25/08/2022
§ first letter of introduction (by a teacher)
optional, the letter must be uploaded by the referee by il 05/09/2022 ore 14.00
§ second letter of introduction (by a teacher)
optional, the letter must be uploaded by the referee by il 05/09/2022 ore 14.00
§ letter of motivation (by the candidate)
optional, the file must be uploaded within 25/08/2022
§ Curriculum Vitae et Studiorum
mandatory
At most two pages, the file must be uploaded within 25/08/2022

Language Skills

the candidate must know the following languages
ENGLISH

Exam Schedule

Qualifications assessment
day06/09/2022
notesnone
publication on notice boardNO
publication on the web siteYes
web sitehttps://phd.uniroma1.it/web/DATA-SCIENCE_nD3565.aspx
date of publication07/09/2022
contactsdottoratods@diag.uniroma1.it

Oral interview
day08/09/2022
notesLe prove continueranno il giorno successivo in Aula B203 ove necessario
time14:00
classroomB222 oppure B203
addressVia Ariosto 25, 00185 Roma, II piano
publication on notice boardNO
publication on the web siteYes
web sitehttps://phd.uniroma1.it/web/DATA-SCIENCE_nD3565.aspx
date of publication12/09/2022
contactsdottoratods@diag.uniroma1.it

Evaluation scale

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

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