Call for application


Educational goals and objectives


PhD:DATA SCIENCE
description:Data Science is an interdisciplinary field of study that has established itself in recent years in order to offer the methodological tools and technologies necessary for the management and analysis of big data and their valorisation in industry, services, and search. The phenomenon of big data has revolutionized countless sectors of economic-social activity. The phenomenon of big data has also profoundly modified the research methodologies and the development of technological innovation in numerous disciplines and applications. The main objective of this PhD is the realization of interdisciplinary research projects of Data Science that lead to the development of innovative methodologies and technologies based on the use of big data in the 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 to understand the impact of big data in applications. These skills are widely represented in the Scientific Disciplinary Sectors that compose the PhD Committee, both with reference to the core competences of Data Science and to the applications indicated above.

Specifications of Scholarships and available positions

Overall Specifications approved for the course by the Academic Senate

Scolarships Consortia External scholarships Positions
4 1 4 10
External organizations scholarships
E-geos SpA1 borsa
Consel-Elis (dottorato industriale)1 borsa
EuResist Network GEIE1 borsa
Note: 1 borsa finanziata dal Dipartimento di Ingegneria Informatica, Automatica e Gestionale "Antonio Ruberti"; 1 borsa finanziata da E-geos SpA - tematica: “Utilizzo dei dati di telerilevamento tramite piattaforme di cloud computing”; 1 borsa finanziata da Cedel - cooperativa sociale educativa ELIS (dottorato industriale); 1 borsa finanziata da EuResist Network GEIE - tematica: "Management and analysis of data for the study of innovative treatments of HIV disease". Dopo la pubblicazione del bando una ulteriore borsa è stata finanziata da Cedel - cooperativa sociale educativa ELIS.

Themes defined for funded scholarships

COMPLESSIVA PER IL CORSO

- Elaborazione del Linguaggio Naturale per la gestione delle risorse umane
Ente finanziatore: Consel-Elis (dottorato industriale)
Dottorato Industriale Consorzio Elis
La borsa di dottorato è rivolta allo sviluppo di nuove tecniche di elaborazione del linguaggio naturale per l'ausilio alla gestione aziendale delle risorse umane. In particolare, sempre più spesso i recruiter delle grandi aziende non riescono a leggere con la dovuta attenzione i curriculum dei candidati senza uno strumento di screening automatizzato dei curriculum. In tale contesto, l’obiettivo della ricerca è finalizzato allo sviluppo di un modello di Linguistica computazionale che possa consentire ai computer di interpretare e sintetizzare al meglio i curriculum vitae nei vari formati in cui si presentano.

- Natural Language Processing for Managing Human Resources
The PhD scholarship is aimed at the development of new natural language processing techniques for the aid to the business management of human resources. In particular, increasingly large company recruiters are unable to read the candidates' curriculum without due attention without an automated curriculum screening tool. In this context, the objective of the research is aimed at developing a model of computational linguistics that can allow computers to interpret and synthesize the curriculum vitae in the various formats in which they are presented

COMPLESSIVA PER IL CORSO

- Utilizzo dei dati di telerilevamento tramite piattaforme di cloud computing
Ente finanziatore: E-geos SpA
Borsa Finanziata E-geos
I dati geospaziali possono essere raccolti e analizzati utilizzando una varietà di sensori e metodologie geomatiche (rilevamento GNSS e terrestre, fotogrammetria e telerilevamento, scansione laser, mappatura mobile, sensori geo-localizzati, contenuti web geo-taggati e informazioni geografiche volontarie-VGI) , ma tra questi quelli relativi al telerilevamento svolgono un ruolo fondamentale, dal momento che gli archivi su petabyte di dati di telerilevamento sono stati resi disponibili gratuitamente dal Programma Copernicus dell'UE e da più agenzie governative statunitensi (NASA, USGS e NOAA). La telerilevazione dei big data è di fondamentale importanza ed è obbligatorio portare "l'intelligenza" (ovvero software e app) dove i dati sono archiviati, e cioè su piattaforme di cloud computing tra cui Google Earth Engine (GEE) è probabilmente la più nota e avanzata. Tuttavia, altre piattaforme di dominio pubblico con obiettivi simili a GEE (cioè ESA DIAS e ESA TEPs - Thematic Exploitation Platforms) sono ora disponibili, insieme a piattaforme private (ovvero OneAtlas Sandbox di Airbus DS e GBDX di DigitalGlobe) create da società che gestiscono sensori satellitari ad alta risoluzione. La ricerca sarà focalizzata sia sulla metodologia che sulle applicazioni utilizzando piattaforme di cloud computing su diverse scale geografiche; possibili argomenti specifici includono, ma non sono limitati, ai seguenti: • Analisi di grandi quantità di dati remoti a distanza e integrazione con altri dati geospaziali (cioè GNSS, dati sui social media) • analisi dei dati multi-sensore e multi-risoluzione • machine e deep learning per il telerilevamento • monitoraggio e modellizzazione del cambiamento dell'uso del suolo e della copertura del suolo • caratterizzazione della dinamica urbana e della popolazione • monitoraggio e modellizzazione delle risorse idriche • monitoraggio e modellazione di foreste e vegetazione dinamica • risposta dell'ecosistema ai cambiamenti climatici

- Big Remote Sensing Data Exploitation through Cloud Computing Platforms
Geospatial data can be collected and analyzed using a variety of geomatic sensors and methodologies (GNSS and terrestrial surveying, photogrammetry and remote sensing, laser scanning, mobile mapping, geo-located sensors, geo-tagged web contents, and volunteered geographic information—VGI), but among them those related to remote sensing play a pivotal role, since petabyte-scale archives of remote sensing data have become freely available from the EU Copernicus Program and multiple U.S. Government agencies (NASA, USGS, and NOAA). Remote sensing big data handling is of key importance, and it is mandatory to bring “the intelligence” (that is software and apps) where data are stored on cloud computing platforms, among which Google Earth Engine (GEE) is probably the most known and advanced. Nevertheless, other public-domain platforms with goals similar to GEE (i.e., ESA DIAS and ESA TEPs—Thematic Exploitation Platforms) are now becoming available, together with private platforms (i.e. OneAtlas Sandbox by Airbus DS and GBDX by DigitalGlobe) established by companies managing high-resolution satellite sensors. Research focusing on both methodology and applications by using cloud computing platforms across different geographic scales are therefore of high interest; possible specific topics include, but are not limited, to the following: • remote rensing big data analysis and integration with other geospatial data (i.e., GNSS, social media data) • multi-sensor and multi-resolution data analysis • machine and deep learning for remote sensing • land-use and land-cover change monitoring and modeling • urban and population dynamics characterization • water resources monitoring and modeling • forests and vegetation dynamics monitoring and modeling • ecosystem response to the climate change

Admission Procedure

Qualifications assessment The Admission Committee assigns to each candidate a maximum score of 60 points. Scores are assigned in accordance with the assessment criteria defined by the Board 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 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 Computer Science and Engineering. 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.


contacts and info email: dottoratods@diag.uniroma1.it Sito Web: http://dottoratods.diag.uniroma1.it/node/5613

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)

Required documentation

research project
The project should not exceed the maximum length of 8000 characters (spaces included).
mandatory
first letter of introduction (by a teacher)
optional, the letter must be uploaded by the candidate
second letter of introduction (by a teacher)
optional, the letter must be uploaded by the candidate
third letter of introduction (by a teacher)
optional, the letter must be uploaded by the candidate
Motivation letter containing the indication of chosen curriculum (by the candidate )
optional

Language Skills

the candidate must know the following languages
ENGLISH

Exam Schedule

Qualifications assessment
day25/07/2019
notesnone
publication on notice boardNO
publication on the web siteYes
web sitehttp://dottoratods.diag.uniroma1.it/admission1920
date of publication25 Luglio 2019
contactsdottoratods@diag.uniroma1.it

Oral interview
day26/07/2019
notesnone
time09:00
classroomB203 - II piano
addressDIAG-Sapienza Via Ariosto 25, Roma
publication on notice boardNO
publication on the web siteYes
web sitehttp://dottoratods.diag.uniroma1.it/admission1920
date of publication29 Luglio 2019
contactsdottoratods@diag.uniroma1.it

Evaluation scale

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

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