Διδακτορικές Σπουδές με Υποτροφία του Εθνικού Κέντρου Ερευνών της Ιταλίας

Εικόνα traptis
Κατηγορία: 

Several PhD positions are open @ IIT-CNR, Pisa, Italy, on the following topics

#1: Analysis of large-scale Online Social Networks (H2020 SoBigData)

#2: Distributed data analytics for IoT (H2020 SoBigData & AUTOWARE)

#3: Social-based Network Traffic Analysis for Cybersecurity (IIT Cybersecurity Lab)

 

** Hosting University: IIT-CNR has multiple agreements for joint PhD programmes with the University of Pisa (https://www.unipi.it/index.php/english) and the University of Florence (http://www.unifi.it/changelang-eng.html).

 

** Position type: doctoral fellowship, 3 years

** Starting date: fall 2017

** Location: IIT-CNR, Pisa, Italy - http://www.iit.cnr.it/ 

** Supervisor: Andrea Passarella - http://cnd.iit.cnr.it/andrea/ 

** Salary: EUR ~1200 per month (net)

** Application deadline: continuous evaluation, up until the end of July 2017

 

For all positions, it will be possible (and advised) to organise one visiting student period abroad (typically, 6 months) during the PhD.

 

 

Position #1: Analysis of large-scale Online Social Networks

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Job description

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The PhD activities will be focused on BigData analytics applied to data crawled from Online Social Networks. Specifically, the subject of the PhD will be on

(i)  collecting large-scale datasets from popular OSNs (e.g., Twitter), and analyse the social network structures and the patterns of interactions between users through Big Data analytics techniques

(ii) designing new data-centric services which exploit knowledge about the extracted social network structures.

 

Successful candidates will be supervised by Dr. Andrea Passarella (http://cnd.iit.cnr.it/andrea), and will work in the framework of the H2020 SoBigData European Project, the EC-funded H2020 Research Infrastructure for social Big Data analysis (http://www.sobigdata.eu/).

 

The PhD activities will involve interdisciplinary approaches focusing on a mix of (i) efficient data crawling and collection techniques, (ii) large-scale data analysis, (iii) knowledge extraction, (iv) design of data centric services in OSN platforms. 

 

 

Candidate profile

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Ideal candidates should have or about to obtain a MSc degree in Computer Science, Computer Engineering, Physics, Statistics, or closely related disciplines, and a proven track record of excellent University grades. Preferably, the topic of the MSc thesis should have been in one of the relevant research areas (BigData analytics, OSN analysis/programming, Complex network analysis). Good written and spoken communication skills in English are required.

 

 

 

 

Position #2: Distributed data analytics for Internet of Things

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Job description

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The expected amount of data generated by pervasive devices in IoT environments calls for new distributed machine learning approaches, which depart from the conventional model of collecting all data in huge data centres where machine learning models are used to extract knowledge. Instead, data analytics is performed on small datasets collected by individual nodes, which then collaborate to learn more complex models. This approach is currently explored, among others by Google in the Federated Learning activity (https://research.google.com/pubs/pub44822.html). It promises to be more scalable, and to better preserve the users' privacy, with respect to centralised machine learning approaches. 

 

One PhD position is open in this area. The PhD activities will be focused on the design and evaluation of distributed data analytics algorithms to be implemented on collaborating sets of networked nodes. Distributed deep learning for Internet of Things environments will be a specific subject of investigation.

 

Successful candidates will be supervised by Dr. Andrea Passarella, and the activities will be carried out in the H2020 FoF AUTOWARE European Project.

 

The PhD will work on a mix of these topics:

(i)   design and prototyping of distributed data analytics algorithms for IoT;

(ii)  evaluation of the performance (e.g., with respect to centralised solutions, in terms of accuracy and generated network traffic);

(iii) analysis of the performance bounds of the distributed analytics algorithms

 

 

Candidate profile

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Ideal candidates should have or about to obtain a MSc in Computer Science, Computer Engineering, Mathematics, or closely related disciplines, and a proven track record of excellent University grades. Preferably, the topic of the MSc thesis should be in one of the relevant research areas (IoT, mobile networking and computing, machine learning, BigData analytics). Good written and spoken communication skills in English are required.

 

 

 

 

 

Position #3: Social-based Network Traffic Analysis for Cybersecurity 

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Job description

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Traditionally, network traffic monitoring tools have focused merely on network-oriented metrics such as volume of data exchanged or top host talkers. Recent cybersecurity attacks instead demonstrated that social relationships have a great impact on network threats. These attacks exploit social relationships such as a shared disk between friends or people belonging to the same working group. To contrast cybersecurity attacks of this kind, novel analysis techniques need to be developed, which do not focus exclusively on packet-level analysis, but correlate traffic patterns with the properties of the nodes generating them (e.g., the same traffic pattern might be legitimate or not, depending on whether the communicating endpoints belong to the same user, to members of the same social community, or to complete strangers).

 

The PhD activities will be focused on (i) learning how social relationships influence network traffic data exchange (ii) designing new social-centric algorithms and techniques that can be used to detect network traffic anomalies as well spot security infections and intrusions, with particular focus on IoT environments, where data must be analysed locally through decentralised algorithms.

 

Successful candidates will be co-supervised by Dr. Andrea Passarella and Dr. Luca Deri, and the activities will be carried out in the framework of the IIT-CNR Cybersecurity Lab.

 

The PhD activities will involve interdisciplinary approaches focusing on a mix of (i) network traffic analysis protocols and tools, (ii) large-scale network metrics analysis, (iii) mapping of social relationship with networks activities, (iv) behaviour-based network traffic modelling.

 

Candidate profile

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Ideal candidates should have or about to obtain a MSc in Computer Science, Computer Engineering, or closely related disciplines, and a proven track record of excellent University grades.  Preferably, the topic of the MSc thesis should be in one of the relevant research areas (IoT, network traffic analysis, network measurement, mobile networking and computing, social networking). Good written and spoken communication skills in English are required.

 

 

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Research group

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The PhD students will work in the Ubiquitous Internet group of IIT-CNR in Pisa, Italy (http://cnd.iit.cnr.it). UI activities range over multiple topics related to the design and analysis of Future Internet networking and computing systems, including data-centric networks, mobile cloud, data analytics, online/mobile social networks, self-organising networks, hybrid wireless/wired networking and computing. The UI group has a strong track record of successful activities in European projects, from FP6 to H2020, which is reflected in the many international collaborations in EU and USA activated by the researchers of the group.

 

 

 

 

Application procedure

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Applications should consist of (all documents in English):

- a complete CV, including exams taken during the University degrees (including

  the MSc final degree), with grades, and a link to the MSc. thesis

- a 1-page research statement showing motivation and understanding

  of the topic of the position

- at least one contact person (2 even better) who could act as reference(s)

 

The applications and any request of information should be sent to: a.passarella@iit.cnr.it , with subject, respectively:

"PhD application: Online Social Network Analysis",

"PhD application: Distributed data analytics for IoT", or

"PhD application: Social-based Network Traffic Analysis for Cybersecurity".

 

Applications will be continuously evaluated upon reception.

Applications will be considered until the position is filled, up until the end of

July 2017. Multiple rounds of interviews will be organised with selected candidates while the positions are open. Interview will be scheduled based on the received applications, possibly also before the end of July 2017.

 

Selected candidates will have to apply for the formal public selections to enter one of the mentioned PhD programmes. Examinations typically take place during Fall (detailed will be provided to selected candidates as soon as decided by the Universities).

 

 

Contact point

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For any additional information or clarification, please send a message to a.passarella@iit.cnr.it

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