Call for application 36° cycle


Call for application 36° cycle
Annex A 36° cycle
Annex B 36° cycle

Specifications of Scholarships and available positions

Overall Specifications approved for the course by the Academic Senate

Scolarships Consortia External scholarships Positions
4 2 2 10
External organizations scholarships
1 borsa finanziata da Fondazione I.S.I. sulla tematica: "Design of algorithms and machine learning methods for the analysis of complex networks"1 borsa
1 borse finanziate dal Dipartimento di Ingegneria Informatica, Automatica e Gestionale "A. Ruberti"1 borse
1 borsa finanziata dalla Fondazione Ugo Bordoni sulla tematica: "Studio del trattamento dei dati della Pubblica Amministrazione utilizzando tecnologie Distributed Ledger"1 borsa
1 borsa finanziate dal Dipartimento di Ingegneria Informatica, Automatica e Gestionale "A. Ruberti" su progetto di ricerca "Sobigdata++"1 borsa

Themes defined for funded scholarships

COMPLESSIVA PER IL CORSO

- Ugo Bordoni: Studio del trattamento dei dati della Pubblica Amministrazione utilizzando tecnologie Distributed Ledger
Institution: 1 borsa finanziata dalla Fondazione Ugo Bordoni
Borsa finanziata da Fondazione Ugo Bordoni
Il dottorato di ricerca avrà come argomento prevalente l’applicabilità della Distributed Ledger Technology ai servizi della Pubblica Amministrazione. In particolare, la ricerca che il dottorando dovrà svolgere riguarderà il trattamento dei dati della pubblica amministrazione in relazione alla decentralizzazione delle infrastrutture, contemplando gli aspetti di interoperabilità tra pubbliche amministrazioni e con i servizi al cittadino e alle imprese, di gestione della privacy dei contenuti presenti nei registri distribuiti, valutando gli impatti economici e finanziari che derivano dalla decentralizzazione dei servizi in comparazione ai sistemi classici centralizzati.

- Study on the management of Public Administration data employing Distributed Ledger technologies
The main objective of the PhD is the study of the applicability of Distributed Ledger Technology to the services of the Public Administration. In particular, the research will focus on the management of the data processed by the Public Administration in a decentralized context. The research will consider aspects of interoperability between public administrations and services for citizens and companies, the privacy of the data stored in distributed ledgers and will also evaluate the economic and financial benefits/drawbacks of decentralization when compared to the classic centralized approach.

COMPLESSIVA PER IL CORSO

- Finanziata da DIAG: Algoritmi di apprendimento automatico per la previsione della progressione delle malattie e degli esiti clinici.
Institution: 1 borsa finanziate dal Dipartimento di Ingegneria Informatica, Automatica e Gestionale "A. Ruberti" su progetto di ricerca "Sobigdata++"
L'apprendimento automatico e l'identificazione di specifici andamenti nei dati clinici sono diventati un'importante strumento della ricerca biomedica per migliorare l'accuratezza della diagnosi e della cura delle malattie. Il monitoraggio degli andamenti e la previsione della progressione della malattia, in particolare durante le fasi iniziali, è anche uno degli obiettivi primari della medicina personalizzata. Il progetto di dottorato mira a sviluppare metodi innovativi di apprendimento automatico per la gestione dei dati eterogenei ed in alta dimensione al fine di offrire alla ricerca medica nuove intuizioni e nuovi approcci sistematici per la classificazione dei pazienti, per la valutazione del rischio clinico e della probabilità di successo dei trattamenti.

- Machine Learning algorithms for predicting disease progression and future clinical outcome
Machine learning and the identification of specific trends in clinical data have become an important tool in biomedical research to improve the accuracy of diagnosis and treatment of diseases. Monitoring trends and predicting disease progression, particularly during the early stages, is also one of the primary goals of personalized medicine. The doctoral project aims to develop innovative machine learning methods for the management of heterogeneous and high-dimensional data in order to offer medical research new insights and new systematic approaches for the classification of patients, for the evaluation of clinical risk and probability. of successful treatments.

COMPLESSIVA PER IL CORSO

- Finanziata da ISI: Progetto di metodi algoritmici e di machine learning per l'analisi di reti complesse
Institution: 1 borsa finanziata da Fondazione I.S.I. sulla tematica: "Design of algorithms and machine learning methods for the analysis of complex networks"
Borsa Finanziata da ISI Foundation
“Nell'attuale contesto di disponibilità di grandi volumi di dati rappresentati come reti complesse, provenienti, per esempio, da reti sociali, biologiche o finanziarie, questa tematica di ricerca si concentra sullo sviluppo di nuovi algoritmi e metodi di machine learning per l'estrazione di conoscenza a supporto delle scienze della complessità e di applicazioni innovative. I metodi sviluppati potranno attingere dall' inferenza statistica, l'analisi causale o l'ottimizzazione combinatoria. Particolare enfasi sarà posta sullo sviluppo di metodi che godano di proprietà di trasparenza e interpretabilità."

- Design of algorithms and machine learning methods for the analysis of complex networks
In the context of the current availability of great volume of data represented in the form of complex networks, such as, for instance social, biological or financial networks, this research theme focuses on the design and development of new algorithms and machine learning methods for the extraction of actionable knowledge supporting innovative applications in science and industry. Our research will build on a variety of different techniques including statistical inference, causal analysis and combinatorial optimization. We will put special emphasis in developing models and methods that are transparent, explainable, and fair-by-design.

COMPLESSIVA PER IL CORSO

- Nessuna tematica specifica
Institution:

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 INGLESE


contacts and info Email: dottoratods@diag.uniroma1.it. Referente Dottorato presso il dipartimento di Ingegneria Informatica, automatica, e Gestionale Antonio Ruberti: Michela Proietti: 06 77274174 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
Scientific interests and motivations
Publications list

Required documentation

§ research project
mandatory
The project should not exceed the maximum length of 8000 characters
(spaces included).
, the file must be uploaded within 23/07/2020

§ first letter of introduction (by a teacher)
optional, the letter must be uploaded by the candidate, the file must be uploaded within 23/07/2020

§ second letter of introduction (by a teacher)
optional, the letter must be uploaded by the candidate, the file must be uploaded within 23/07/2020

§ third letter of introduction (by a teacher)
optional, the letter must be uploaded by the candidate, the file must be uploaded within 23/07/2020

§ Motivation letter containing the indication of chosen curriculum (by the candidate )
optional
At most 4000 characters (spazi inclusi). Indicate specific topic of interests among the fellowships funded by external bodies or research projects. , the file must be uploaded within 23/07/2020

Language Skills

the candidate must know the following languages
ENGLISH

Exam Schedule

Qualifications assessment
day07/09/2020
notesnone
publication on notice boardNO
publication on the web siteYes
web sitehttps://phd.uniroma1.it/web/DATA-SCIENCE_nD3565_IT.aspx
date of publication08/09/2020
contactsdottoratods@diag.uniroma1.it

Oral interview
day11/09/2020
notesnone
time09:00
classroomB203 - oppure in remoto tramite Meet: meet.google.com/isw-ftnq-oor
addressVia Ariosto 25
publication on notice boardNO
publication on the web siteYes
web sitehttps://phd.uniroma1.it/web/DATA-SCIENCE_nD3565_IT.aspx
date of publication13/09/2020
contactsdottoratods@diag.uniroma1.it

Evaluation scale

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

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